{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"BatchNormalization.ipynb","provenance":[],"collapsed_sections":[]},"language_info":{"name":"python"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xH-0r_-9TzB2","executionInfo":{"status":"ok","timestamp":1619618306680,"user_tz":-480,"elapsed":2275,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"520c5245-a025-486a-e351-99516072e021"},"source":["# This mounts your Google Drive to the Colab VM.\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","# TODO: Enter the foldername in your Drive where you have saved the unzipped\n","# assignment folder, e.g. 'cs231n/assignments/assignment1/'\n","FOLDERNAME = 'cs231n/assignments/assignment2/'\n","assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n","\n","# Now that we've mounted your Drive, this ensures that\n","# the Python interpreter of the Colab VM can load\n","# python files from within it.\n","import sys\n","sys.path.append('/content/drive/My Drive/{}'.format(FOLDERNAME))\n","\n","# This downloads the CIFAR-10 dataset to your Drive\n","# if it doesn't already exist.\n","%cd /content/drive/My\\ Drive/$FOLDERNAME/cs231n/datasets/\n","!bash get_datasets.sh\n","%cd /content/drive/My\\ Drive/$FOLDERNAME"],"execution_count":60,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n","/content/drive/My Drive/cs231n/assignments/assignment2/cs231n/datasets\n","/content/drive/My Drive/cs231n/assignments/assignment2\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"tags":["pdf-title"],"id":"hc3wZh1UTzB9"},"source":["# Batch Normalization\n","One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization, proposed by [1] in 2015.\n","\n","To understand the goal of batch normalization, it is important to first recognize that machine learning methods tend to perform better with input data consisting of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features. This will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance, since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n","\n","The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, they propose to insert into the network layers that normalize batches. At training time, such a layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n","\n","It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n","\n","[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n","Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"]},{"cell_type":"code","metadata":{"tags":["pdf-ignore"],"colab":{"base_uri":"https://localhost:8080/"},"id":"L9FQVJoiTzB-","executionInfo":{"status":"ok","timestamp":1619618393137,"user_tz":-480,"elapsed":827,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"3f7e253c-2204-47e5-a241-4e50804cb4d8"},"source":["# Setup cell.\n","import time\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from cs231n.classifiers.fc_net import *\n","from cs231n.data_utils import get_CIFAR10_data\n","from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n","from cs231n.solver import Solver\n","\n","%matplotlib inline\n","plt.rcParams[\"figure.figsize\"] = (10.0, 8.0)  # Set default size of plots.\n","plt.rcParams[\"image.interpolation\"] = \"nearest\"\n","plt.rcParams[\"image.cmap\"] = \"gray\"\n","\n","%load_ext autoreload\n","%autoreload 2\n","\n","def rel_error(x, y):\n","    \"\"\"Returns relative error.\"\"\"\n","    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n","\n","def print_mean_std(x,axis=0):\n","    print(f\"  means: {x.mean(axis=axis)}\")\n","    print(f\"  stds:  {x.std(axis=axis)}\\n\")"],"execution_count":61,"outputs":[{"output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n","  %reload_ext autoreload\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"tags":["pdf-ignore"],"colab":{"base_uri":"https://localhost:8080/"},"id":"h0ply5BLTzB-","executionInfo":{"status":"ok","timestamp":1619618405204,"user_tz":-480,"elapsed":10703,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"f56c819d-5b4e-459f-ab94-4a34c59ff422"},"source":["# Load the (preprocessed) CIFAR-10 data.\n","data = get_CIFAR10_data()\n","for k, v in list(data.items()):\n","    print(f\"{k}: {v.shape}\")"],"execution_count":62,"outputs":[{"output_type":"stream","text":["X_train: (49000, 3, 32, 32)\n","y_train: (49000,)\n","X_val: (1000, 3, 32, 32)\n","y_val: (1000,)\n","X_test: (1000, 3, 32, 32)\n","y_test: (1000,)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"vZ_UKnIbTzB-"},"source":["# Batch Normalization: Forward Pass\n","In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n","\n","Referencing the paper linked to above in [1] may be helpful!"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qLDH3wr-TzB_","executionInfo":{"status":"ok","timestamp":1619610116250,"user_tz":-480,"elapsed":1041,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"2e1a977f-b04b-47c5-bd09-9407f7e7d438"},"source":["# Check the training-time forward pass by checking means and variances\n","# of features both before and after batch normalization   \n","\n","# Simulate the forward pass for a two-layer network.\n","np.random.seed(231)\n","N, D1, D2, D3 = 200, 50, 60, 3\n","X = np.random.randn(N, D1)\n","W1 = np.random.randn(D1, D2)\n","W2 = np.random.randn(D2, D3)\n","a = np.maximum(0, X.dot(W1)).dot(W2)\n","\n","print('Before batch normalization:')\n","print_mean_std(a,axis=0)\n","\n","gamma = np.ones((D3,))\n","beta = np.zeros((D3,))\n","\n","# Means should be close to zero and stds close to one.\n","print('After batch normalization (gamma=1, beta=0)')\n","a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n","print_mean_std(a_norm,axis=0)\n","\n","gamma = np.asarray([1.0, 2.0, 3.0])\n","beta = np.asarray([11.0, 12.0, 13.0])\n","\n","# Now means should be close to beta and stds close to gamma.\n","print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n","a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n","print_mean_std(a_norm,axis=0)"],"execution_count":36,"outputs":[{"output_type":"stream","text":["Before batch normalization:\n","  means: [ -2.3814598  -13.18038246   1.91780462]\n","  stds:  [27.18502186 34.21455511 37.68611762]\n","\n","After batch normalization (gamma=1, beta=0)\n","  means: [5.32907052e-17 7.04991621e-17 1.85962357e-17]\n","  stds:  [0.99999999 1.         1.        ]\n","\n","After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n","  means: [11. 12. 13.]\n","  stds:  [0.99999999 1.99999999 2.99999999]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Vn0ewWgsTzCA","executionInfo":{"status":"ok","timestamp":1619610119713,"user_tz":-480,"elapsed":817,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"a0c66e89-d0cc-40a5-da09-97924a53ec11"},"source":["# Check the test-time forward pass by running the training-time\n","# forward pass many times to warm up the running averages, and then\n","# checking the means and variances of activations after a test-time\n","# forward pass.\n","\n","np.random.seed(231)\n","N, D1, D2, D3 = 200, 50, 60, 3\n","W1 = np.random.randn(D1, D2)\n","W2 = np.random.randn(D2, D3)\n","\n","bn_param = {'mode': 'train'}\n","gamma = np.ones(D3)\n","beta = np.zeros(D3)\n","\n","for t in range(50):\n","  X = np.random.randn(N, D1)\n","  a = np.maximum(0, X.dot(W1)).dot(W2)\n","  batchnorm_forward(a, gamma, beta, bn_param)\n","\n","bn_param['mode'] = 'test'\n","X = np.random.randn(N, D1)\n","a = np.maximum(0, X.dot(W1)).dot(W2)\n","a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n","\n","# Means should be close to zero and stds close to one, but will be\n","# noisier than training-time forward passes.\n","print('After batch normalization (test-time):')\n","print_mean_std(a_norm,axis=0)"],"execution_count":37,"outputs":[{"output_type":"stream","text":["After batch normalization (test-time):\n","  means: [-0.03927354 -0.04349152 -0.10452688]\n","  stds:  [1.01531427 1.01238373 0.97819987]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"UwkePgWOTzCB"},"source":["# Batch Normalization: Backward Pass\n","Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n","\n","To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n","\n","Once you have finished, run the following to numerically check your backward pass."]},{"cell_type":"code","metadata":{"id":"P7MOCh8sTzCB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619610125942,"user_tz":-480,"elapsed":808,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"1155f33b-c32f-4525-fd8d-99c35ddd950a"},"source":["# Gradient check batchnorm backward pass.\n","np.random.seed(231)\n","N, D = 4, 5\n","x = 5 * np.random.randn(N, D) + 12\n","gamma = np.random.randn(D)\n","beta = np.random.randn(D)\n","dout = np.random.randn(N, D)\n","\n","bn_param = {'mode': 'train'}\n","fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n","fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n","fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n","\n","dx_num = eval_numerical_gradient_array(fx, x, dout)\n","da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n","db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n","\n","_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n","dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n","\n","# You should expect to see relative errors between 1e-13 and 1e-8.\n","print('dx error: ', rel_error(dx_num, dx))\n","print('dgamma error: ', rel_error(da_num, dgamma))\n","print('dbeta error: ', rel_error(db_num, dbeta))"],"execution_count":38,"outputs":[{"output_type":"stream","text":["dx error:  1.7029241291468676e-09\n","dgamma error:  7.420414216247087e-13\n","dbeta error:  2.8795057655839487e-12\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"s6-gFDVnTzCC"},"source":["# Batch Normalization: Alternative Backward Pass\n","In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n","\n","Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n","\n","In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n","\n","we first calculate the mean $\\mu$ and variance $v$.\n","With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n","The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n","\n","\\begin{align}\n","& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n","& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n","\\end{align}"]},{"cell_type":"markdown","metadata":{"id":"viuUo17bTzCC"},"source":["<img src=\"https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/assets/a2/batchnorm_graph.png\">"]},{"cell_type":"markdown","metadata":{"tags":["pdf-ignore"],"id":"Xw4XXG8aTzCC"},"source":["The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n","\n","The unknown/hard part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n","$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n","$\\frac{\\partial \\sigma}{\\partial v}$, \n","$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n","and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n","\n","If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n","\n","You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n","\n","After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."]},{"cell_type":"code","metadata":{"id":"N2-uSuWhTzCD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619577093329,"user_tz":-480,"elapsed":1049,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"6951fbc1-d3dd-4713-f2df-ce05132c2922"},"source":["np.random.seed(231)\n","N, D = 100, 500\n","x = 5 * np.random.randn(N, D) + 12\n","gamma = np.random.randn(D)\n","beta = np.random.randn(D)\n","dout = np.random.randn(N, D)\n","\n","bn_param = {'mode': 'train'}\n","out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n","\n","t1 = time.time()\n","dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n","t2 = time.time()\n","dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n","t3 = time.time()\n","\n","print('dx difference: ', rel_error(dx1, dx2))\n","print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n","print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n","print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dx difference:  9.20004371222927e-13\n","dgamma difference:  0.0\n","dbeta difference:  0.0\n","speedup: 2.01x\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Ddtj5oc-TzCD"},"source":["# Fully Connected Networks with Batch Normalization\n","Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n","\n","Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n","\n","**Hint:** You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`."]},{"cell_type":"code","metadata":{"id":"zD-b-YllTzCD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619598207612,"user_tz":-480,"elapsed":4185,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"6be1cc7f-8dc7-4b3b-bcbd-a607f9467449"},"source":["np.random.seed(231)\n","N, D, H1, H2, C = 2, 15, 20, 30, 10\n","X = np.random.randn(N, D)\n","y = np.random.randint(C, size=(N,))\n","\n","# You should expect losses between 1e-4~1e-10 for W, \n","# losses between 1e-08~1e-10 for b,\n","# and losses between 1e-08~1e-09 for beta and gammas.\n","for reg in [0, 3.14]:\n","  print('Running check with reg = ', reg)\n","  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n","                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n","                            normalization='batchnorm')\n","\n","  loss, grads = model.loss(X, y)\n","  print('Initial loss: ', loss)\n","\n","  for name in sorted(grads):\n","    f = lambda _: model.loss(X, y)[0]\n","    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n","    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n","  if reg == 0: print()"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Running check with reg =  0\n","Initial loss:  2.2611955101340957\n","W1 relative error: 1.10e-04\n","W2 relative error: 5.65e-06\n","W3 relative error: 4.14e-10\n","b1 relative error: 4.44e-08\n","b2 relative error: 5.55e-09\n","b3 relative error: 1.02e-10\n","beta1 relative error: 7.33e-09\n","beta2 relative error: 1.17e-09\n","gamma1 relative error: 7.47e-09\n","gamma2 relative error: 3.35e-09\n","\n","Running check with reg =  3.14\n","Initial loss:  6.996533220108303\n","W1 relative error: 1.98e-06\n","W2 relative error: 2.28e-06\n","W3 relative error: 1.11e-08\n","b1 relative error: 5.55e-09\n","b2 relative error: 2.22e-08\n","b3 relative error: 1.73e-10\n","beta1 relative error: 6.65e-09\n","beta2 relative error: 3.48e-09\n","gamma1 relative error: 6.27e-09\n","gamma2 relative error: 4.67e-09\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HnLxPmRUTzCE"},"source":["# Batch Normalization for Deep Networks\n","Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."]},{"cell_type":"code","metadata":{"id":"TbJiOG2jTzCE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619598319222,"user_tz":-480,"elapsed":14214,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"c439296c-2e5d-4ca9-d7a8-7fd2196d8cc3"},"source":["np.random.seed(231)\n","\n","# Try training a very deep net with batchnorm.\n","hidden_dims = [100, 100, 100, 100, 100]\n","\n","num_train = 1000\n","small_data = {\n","  'X_train': data['X_train'][:num_train],\n","  'y_train': data['y_train'][:num_train],\n","  'X_val': data['X_val'],\n","  'y_val': data['y_val'],\n","}\n","\n","weight_scale = 2e-2\n","bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n","model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n","\n","print('Solver with batch norm:')\n","bn_solver = Solver(bn_model, small_data,\n","                num_epochs=10, batch_size=50,\n","                update_rule='adam',\n","                optim_config={\n","                  'learning_rate': 1e-3,\n","                },\n","                verbose=True,print_every=20)\n","bn_solver.train()\n","\n","print('\\nSolver without batch norm:')\n","solver = Solver(model, small_data,\n","                num_epochs=10, batch_size=50,\n","                update_rule='adam',\n","                optim_config={\n","                  'learning_rate': 1e-3,\n","                },\n","                verbose=True, print_every=20)\n","solver.train()"],"execution_count":17,"outputs":[{"output_type":"stream","text":["Solver with batch norm:\n","(Iteration 1 / 200) loss: 2.340974\n","(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.115000\n","(Epoch 1 / 10) train acc: 0.314000; val_acc: 0.266000\n","(Iteration 21 / 200) loss: 2.039345\n","(Epoch 2 / 10) train acc: 0.395000; val_acc: 0.280000\n","(Iteration 41 / 200) loss: 2.047471\n","(Epoch 3 / 10) train acc: 0.484000; val_acc: 0.316000\n","(Iteration 61 / 200) loss: 1.739554\n","(Epoch 4 / 10) train acc: 0.524000; val_acc: 0.318000\n","(Iteration 81 / 200) loss: 1.246973\n","(Epoch 5 / 10) train acc: 0.595000; val_acc: 0.335000\n","(Iteration 101 / 200) loss: 1.354766\n","(Epoch 6 / 10) train acc: 0.637000; val_acc: 0.331000\n","(Iteration 121 / 200) loss: 1.014048\n","(Epoch 7 / 10) train acc: 0.673000; val_acc: 0.323000\n","(Iteration 141 / 200) loss: 1.135644\n","(Epoch 8 / 10) train acc: 0.682000; val_acc: 0.303000\n","(Iteration 161 / 200) loss: 0.652241\n","(Epoch 9 / 10) train acc: 0.781000; val_acc: 0.336000\n","(Iteration 181 / 200) loss: 0.782712\n","(Epoch 10 / 10) train acc: 0.750000; val_acc: 0.321000\n","\n","Solver without batch norm:\n","(Iteration 1 / 200) loss: 2.302332\n","(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n","(Epoch 1 / 10) train acc: 0.283000; val_acc: 0.250000\n","(Iteration 21 / 200) loss: 2.041970\n","(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.277000\n","(Iteration 41 / 200) loss: 1.900473\n","(Epoch 3 / 10) train acc: 0.373000; val_acc: 0.282000\n","(Iteration 61 / 200) loss: 1.713156\n","(Epoch 4 / 10) train acc: 0.390000; val_acc: 0.310000\n","(Iteration 81 / 200) loss: 1.662209\n","(Epoch 5 / 10) train acc: 0.434000; val_acc: 0.300000\n","(Iteration 101 / 200) loss: 1.696059\n","(Epoch 6 / 10) train acc: 0.535000; val_acc: 0.345000\n","(Iteration 121 / 200) loss: 1.557987\n","(Epoch 7 / 10) train acc: 0.530000; val_acc: 0.304000\n","(Iteration 141 / 200) loss: 1.432189\n","(Epoch 8 / 10) train acc: 0.628000; val_acc: 0.339000\n","(Iteration 161 / 200) loss: 1.034116\n","(Epoch 9 / 10) train acc: 0.654000; val_acc: 0.342000\n","(Iteration 181 / 200) loss: 0.905795\n","(Epoch 10 / 10) train acc: 0.712000; val_acc: 0.328000\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"9wizFF6oTzCE"},"source":["Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."]},{"cell_type":"code","metadata":{"tags":["pdf-ignore-input"],"id":"6hZuRQ1yTzCE","colab":{"base_uri":"https://localhost:8080/","height":642},"executionInfo":{"status":"ok","timestamp":1619598353511,"user_tz":-480,"elapsed":2029,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"c1a5641c-2240-4861-8019-0ba96343d3d0"},"source":["def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n","    \"\"\"utility function for plotting training history\"\"\"\n","    plt.title(title)\n","    plt.xlabel(label)\n","    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n","    bl_plot = plot_fn(baseline)\n","    num_bn = len(bn_plots)\n","    for i in range(num_bn):\n","        label='with_norm'\n","        if labels is not None:\n","            label += str(labels[i])\n","        plt.plot(bn_plots[i], bn_marker, label=label)\n","    label='baseline'\n","    if labels is not None:\n","        label += str(labels[0])\n","    plt.plot(bl_plot, bl_marker, label=label)\n","    plt.legend(loc='lower center', ncol=num_bn+1) \n","\n","    \n","plt.subplot(3, 1, 1)\n","plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n","                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n","plt.subplot(3, 1, 2)\n","plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n","                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n","plt.subplot(3, 1, 3)\n","plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n","                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n","\n","plt.gcf().set_size_inches(15, 15)\n","plt.show()"],"execution_count":18,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x1080 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"sWQYmh80TzCF"},"source":["# Batch Normalization and Initialization\n","We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n","\n","The first cell will train eight-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."]},{"cell_type":"code","metadata":{"tags":["pdf-ignore-input"],"id":"fIrRGDmbTzCF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619598557982,"user_tz":-480,"elapsed":162752,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"49f43d6f-dd99-4986-f674-702e66509c52"},"source":["np.random.seed(231)\n","\n","# Try training a very deep net with batchnorm.\n","hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n","num_train = 1000\n","small_data = {\n","  'X_train': data['X_train'][:num_train],\n","  'y_train': data['y_train'][:num_train],\n","  'X_val': data['X_val'],\n","  'y_val': data['y_val'],\n","}\n","\n","bn_solvers_ws = {}\n","solvers_ws = {}\n","weight_scales = np.logspace(-4, 0, num=20)\n","for i, weight_scale in enumerate(weight_scales):\n","    print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n","    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n","    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n","\n","    bn_solver = Solver(bn_model, small_data,\n","                  num_epochs=10, batch_size=50,\n","                  update_rule='adam',\n","                  optim_config={\n","                    'learning_rate': 1e-3,\n","                  },\n","                  verbose=False, print_every=200)\n","    bn_solver.train()\n","    bn_solvers_ws[weight_scale] = bn_solver\n","\n","    solver = Solver(model, small_data,\n","                  num_epochs=10, batch_size=50,\n","                  update_rule='adam',\n","                  optim_config={\n","                    'learning_rate': 1e-3,\n","                  },\n","                  verbose=False, print_every=200)\n","    solver.train()\n","    solvers_ws[weight_scale] = solver"],"execution_count":19,"outputs":[{"output_type":"stream","text":["Running weight scale 1 / 20\n","Running weight scale 2 / 20\n","Running weight scale 3 / 20\n","Running weight scale 4 / 20\n","Running weight scale 5 / 20\n","Running weight scale 6 / 20\n","Running weight scale 7 / 20\n","Running weight scale 8 / 20\n","Running weight scale 9 / 20\n","Running weight scale 10 / 20\n","Running weight scale 11 / 20\n","Running weight scale 12 / 20\n","Running weight scale 13 / 20\n","Running weight scale 14 / 20\n","Running weight scale 15 / 20\n","Running weight scale 16 / 20\n"],"name":"stdout"},{"output_type":"stream","text":["/content/drive/My Drive/cs231n/assignments/assignment2/cs231n/layers.py:150: RuntimeWarning: divide by zero encountered in log\n","  loss = -np.sum(np.log(normalized_scores[np.arange(num_train), y]))\n"],"name":"stderr"},{"output_type":"stream","text":["Running weight scale 17 / 20\n","Running weight scale 18 / 20\n","Running weight scale 19 / 20\n","Running weight scale 20 / 20\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"tags":["pdf-ignore-input"],"id":"FFM5cunxTzCF","colab":{"base_uri":"https://localhost:8080/","height":630},"executionInfo":{"status":"ok","timestamp":1619598635254,"user_tz":-480,"elapsed":2829,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"4feda77c-bc6d-4bd3-cba7-ec4538e32b03"},"source":["# Plot results of weight scale experiment.\n","best_train_accs, bn_best_train_accs = [], []\n","best_val_accs, bn_best_val_accs = [], []\n","final_train_loss, bn_final_train_loss = [], []\n","\n","for ws in weight_scales:\n","  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n","  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n","  \n","  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n","  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n","  \n","  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n","  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n","  \n","plt.subplot(3, 1, 1)\n","plt.title('Best val accuracy vs. weight initialization scale')\n","plt.xlabel('Weight initialization scale')\n","plt.ylabel('Best val accuracy')\n","plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n","plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n","plt.legend(ncol=2, loc='lower right')\n","\n","plt.subplot(3, 1, 2)\n","plt.title('Best train accuracy vs. weight initialization scale')\n","plt.xlabel('Weight initialization scale')\n","plt.ylabel('Best training accuracy')\n","plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n","plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n","plt.legend()\n","\n","plt.subplot(3, 1, 3)\n","plt.title('Final training loss vs. weight initialization scale')\n","plt.xlabel('Weight initialization scale')\n","plt.ylabel('Final training loss')\n","plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n","plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n","plt.legend()\n","plt.gca().set_ylim(1.0, 3.5)\n","\n","plt.gcf().set_size_inches(15, 15)\n","plt.show()"],"execution_count":20,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x1080 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"tags":["pdf-inline"],"id":"Asatv_ToTzCF"},"source":["## Inline Question 1:\n","Describe the results of this experiment. How does the weight initialization scale affect models with/without batch normalization differently, and why?\n","\n","## Answer:\n","[FILL THIS IN]\n"]},{"cell_type":"markdown","metadata":{"id":"gxYfDZPBTzCG"},"source":["# Batch Normalization and Batch Size\n","We will now run a small experiment to study the interaction of batch normalization and batch size.\n","\n","The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."]},{"cell_type":"code","metadata":{"tags":["pdf-ignore-input"],"id":"D5evQ_iLTzCG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619599149712,"user_tz":-480,"elapsed":101698,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"7fd07af1-3002-460d-8eed-fc28177d5c66"},"source":["def run_batchsize_experiments(normalization_mode):\n","    np.random.seed(231)\n","    \n","    # Try training a very deep net with batchnorm.\n","    hidden_dims = [100, 100, 100, 100, 100]\n","    num_train = 1000\n","    small_data = {\n","      'X_train': data['X_train'][:num_train],\n","      'y_train': data['y_train'][:num_train],\n","      'X_val': data['X_val'],\n","      'y_val': data['y_val'],\n","    }\n","    n_epochs=10\n","    weight_scale = 2e-2\n","    batch_sizes = [5,10,50]\n","    lr = 10**(-3.5)\n","    solver_bsize = batch_sizes[0]\n","\n","    print('No normalization: batch size = ',solver_bsize)\n","    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n","    solver = Solver(model, small_data,\n","                    num_epochs=n_epochs, batch_size=solver_bsize,\n","                    update_rule='adam',\n","                    optim_config={\n","                      'learning_rate': lr,\n","                    },\n","                    verbose=False)\n","    solver.train()\n","    \n","    bn_solvers = []\n","    for i in range(len(batch_sizes)):\n","        b_size=batch_sizes[i]\n","        print('Normalization: batch size = ',b_size)\n","        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n","        bn_solver = Solver(bn_model, small_data,\n","                        num_epochs=n_epochs, batch_size=b_size,\n","                        update_rule='adam',\n","                        optim_config={\n","                          'learning_rate': lr,\n","                        },\n","                        verbose=False)\n","        bn_solver.train()\n","        bn_solvers.append(bn_solver)\n","        \n","    return bn_solvers, solver, batch_sizes\n","\n","batch_sizes = [5,10,50]\n","bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"],"execution_count":26,"outputs":[{"output_type":"stream","text":["No normalization: batch size =  5\n","Normalization: batch size =  5\n","Normalization: batch size =  10\n","Normalization: batch size =  50\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"re6ou9ydTzCG","colab":{"base_uri":"https://localhost:8080/","height":621},"executionInfo":{"status":"ok","timestamp":1619599199341,"user_tz":-480,"elapsed":1859,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"be18d06e-66fc-4c1c-e8d8-f260e7afe80d"},"source":["plt.subplot(2, 1, 1)\n","plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n","                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n","plt.subplot(2, 1, 2)\n","plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n","                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n","\n","plt.gcf().set_size_inches(15, 10)\n","plt.show()"],"execution_count":27,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA3AAAAJcCAYAAAC480YuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXycV33v8c+ZRcvIWqx1tHh37Di2JdnR4gBZDSHUpCkU0gQIhFJS6A24LeUWaJs43ELpLZBrCuHelNJS2kJToDTgtKwFCklseZHk3XEcO7asXdZiaUaa5dw/ntFIo8WblpHk7/v10kszzzzLeUayrK/OOb9jrLWIiIiIiIjI3OdKdgNERERERETkyijAiYiIiIiIzBMKcCIiIiIiIvOEApyIiIiIiMg8oQAnIiIiIiIyTyjAiYiIiIiIzBMKcCIiSWSM+Q9jzHume1+ZnDHmV8aYTUm47g5jzD/O9nVnijHmtDHm9bHHnzDGfGUGrvF/jTF/Ng3nudcY8y/T0SYRkWRTgBMRuUrGmIujPqLGmMCo5++8mnNZa99krf3adO8rEzPG3Av0WWsPxJ7vMMaERn39jhpjfvMqzvczY8zvzFBbrTHmoDHGNWrbnxtj/n4mrjcV1tpPW2un9D4YYx42xvxyzHk/YK39X1NrHVhrvwesN8aUT/VcIiLJpgAnInKVrLWLhj+AV4F7R237p+H9jDGe5LVy/pjl9+kDwNfHbPuXUV/P3wf+0RhTNIttupQS4IGpnkTfiwB8A3gk2Y0QEZkqBTgRkWlijLnDGHPOGPPHxpgW4O+MMYuNMd83xrQbYy7EHpeNOibegzPcA2GM+Wxs31eMMW+6xn1XGGN+YYzpM8b82BjzpcmG711BG3ONMX9njDkfe/27o167zxhTb4zpNca8bIy5J7Y9Prwu9jw+fNAYszzWu/Q+Y8yrwE9j2//VGNNijOmJtX39qOPTjTGfM8acib3+y9i2XcaYD425n0ZjzFsmuM8U4C7g55N9Da21PwD6gFWXe2+MMZ8CbgW+GOu9+2Js+3pjzI+MMV3GmFZjzCdGXSLFGPMPsa/LYWNM1WRtifnfwBOTBTBjzK/HztMd+/5YN+q107HvxUag3xizOva+v9cYczZ2Px8wxlTH3rPu4XuIHb/KGPNTY0ynMabDGPNPxpicSdox+us7/H4Mf4SNMTtir30s9n3SZ4w5Mvx1irX7/wK3xI7pjm3/e2PMn4+6zvuNMSdj7+2zxpiSUa/Z2P28FLuXLxljzKhm/gzYdpn3W0RkzlOAExGZXn4gF1iG89d+F/B3sedLgQDwxUmPhlrgOJCP88v73475JfRK9/1nYA+QB+wAHrrENS/Xxq8DPmA9UAg8CWCMqQH+AfgokAPcBpy+xHXGuh1YB7wx9vw/gBti19gP/NOofT8L3Ay8Buf9/Z9AFPga8K7hnYwxFUApsGuC690ARK215yZqjHFsA1KAI7HNk7431to/Af4beDTWg/eoMSYT+DHwnzi9Z6uBn4y6zK8D38R5v57l0t8LAN8BeoGHJ2jvGpxepd8HCoDngO/FguqwB3FCSw4Qjm2rjb0XvwX8H+BPgNfjfH3vN8bcPnwJ4C9i97EOWILzvXRJ1tpHR/Vovg64APx77OWXcUJvNvAETm9nsbX2KE7v6AuxY8cFRWPMXbH23A8UA2dw3svR3gxUA+Wx/d446rWjwHJjTNbl7kFEZC5TgBMRmV5R4HFr7aC1NmCt7bTWfttaO2Ct7QM+hRNcJnPGWvs31toITjgpBiYbzjfhvsaYpTi/xD5mrR2y1v4SJyxM6FJtNMYUA28CPmCtvWCtDVlrh3uw3gd81Vr7I2tt1FrbZK09dmVvEwA7rLX91tpArB1ftdb2WWsHcYJChTEm2zhzwH4b2B67RsRa+3xsv2eBNcaYG2LnfAhnSOTQBNfLweldG+v+WI/Pxdj5Pm2t7b7cezOJNwMt1trPWWuDsfvZPer1X1prn4t9zb4OVFzmPbLAnwF/NiaYgRPAdsXe/xBOyE3HCbnDvmCtPTv8Hsf8r1jbfgj0A9+w1rZZa5twAumm2L2fjJ170FrbDnz+MveewBhTAHwX+NDwnENr7b9aa8/Hvl/+BXgJqLnCU74T5/ttf+xr/3GcHrvlo/b5jLW221r7KvBfQOWo14a/9hP2IoqIzBcKcCIi06vdWhscfmKM8Rlj/l9s6F8v8AsgxxjjnuT4luEH1tqB2MNFV7lvCdA1ahvA2ckafJk2Lomd68IEhy7B6VG5VvE2GWPcxpjPxIbX9TLSk5cf+0ib6Fqx9/pfgHfFgt6DjJ/jNuwCkDnB9mestTnW2gycoZPvNsb8bqxdV/v1u9x70jLq8QCQNtnwyGHW2ueAc8DvjnmpBKcXani/KM57Wjpqn4m+7q2jHgcmeL4IwBhTZIz5pjGmKXbv/4jztbgsY4wX+Bbwz9bab47a/m7jDLntjoXmDVd6Tsbf70Wgk8T7Hfv+jv63M/y1777C64mIzEkKcCIi08uOef4RYC1Qa63NwhlmCM7wtJnSDOQaY3yjti25xP6XauPZ2Lkm6rU4S2yu2AT6cYZdDvNPsM/o9+odwH04Q/mygeWj2tABBC9xra/h9M5sBQastS9Mst9JnJGSpZO8jrX2NM5Qzntjmy739Rv79T4LrJzs/FPwJ8AnSHxPz+MM7XQa5AyfXQI0jdpnbPuuxqdjx2+M3fu7uPLv27/GGfr5p6Patwz4G+BRIC82TPIQk7+XY4293wycIcJNkx6RaB1w2lrbe4X7i4jMSQpwIiIzKxOnV6PbGJMLPD7TF7TWngH2AjuMMSnGmFsYCSRX1UZrbTNOoHnKOAU9vMaY4RDzt8B7jTFbjTEuY0ypMebG2Gv1wAOx/auAt12m2ZnAIE6Pig8nPAy3IQp8Ffi8MaYk1lt3izEmNfb6CzhDVz/H5L1vxIZV/phLDAM0ToGSe4DDl3tvYlpJDGzfB4qNMb9vjEk1xmQaY2ovc++XZa39GU7YGb0O4DPAttj778UJm4PA81O9XkwmzrDSnljo/eiVHBTrvbwdeGfsazcsAyektcf2ey9OD9ywVqBsgqGiw76B8/1WGfvafxrYHQvdV+J2nO9lEZF5TQFORGRm/R+ceUkdwIs4xS1mwzuBW3AC0Z/jDDMcnGTfy7XxISAEHAPacIpmYK3dA7wXp6hJD051x+Eekj/D6TG7gFOs4p8v095/wBke14RTQOTFMa//EXAQqAO6gL8k8f+wfwA24gzzu5T/x/iCLr81XDExdv5fxdoMl39vdgJvM05Fxy/E5sm9AScwt+DM8brzMm26Un+KU8AFAGvtcZxesb+Ote9enCUtJpr/dy2eADbjfG134RRUuRIP4oTa82akEuUnrLVHcEL2CzhhbSPOez3spzjBucUY0zH2pNbaH+N8X30bp5d5FVe3xMKDOF9/EZF5zVg7ldEVIiIyHxhj/gU4Zq2d8R7AZDDGvBt4xFr7uivY91c4lSMPzHzLZC4wzgLuD1lr7092W0REpkoBTkRkATLGVOP0VL0C3I1TDfCWhRhaYnP9fgo8Za39h2S3R0REZCZpCKWIyMLkx1m4+CLwBeCDCzS8vRFnTlUrlx+mKSIiMu+pB05ERERERGSeUA+ciIiIiIjIPHHJxUOTIT8/3y5fvjzZzRAREREREUmKffv2dVhrCyZ6bc4FuOXLl7N3795kN0NERERERCQpjDFnJntNQyhFRERERETmCQU4ERERERGReUIBTkREREREZJ5QgBMREREREZknFOBERERERETmCQU4ERERERGReUIBTkREREREriu7Tu3i7m/dTfnXyrn7W3ez69SuZDfpis25deBERERERERmyq5Tu9jx/A6CkSAAzf3N7Hh+BwDbVm5LYsuujHrgRERERETkumCt5cl9T8bD27BgJMjO/TuT1Kqrox44ERERERGZtyLRCBcGL9AZ6KQr2EVnsJPOQGf8c1ewK/68K9hFOBqe8Dwt/S2z3PJrowAnIiIiIiJzylBkKDGMjQpgY8NZ92A3URsddw6Py0NeWh556c7HmsVryEvP41snvkXvUO+4/f0Z/tm4tSlTgBMRERERkRk3EBq4ZO/Y6B60vqG+Cc+R7kknLy2P3PRclmQuobKw0nmelusEtVhgy03LJSslC2PMuHOsWbwmYQ4cQJo7je2bt8/YvU8nBTgREREREblq1lp6h3ovG8aGHwfCgQnPk5WSFQ9gw71kEwWyvLQ8fF7flNs9XKhk5/6dtPS34M/ws33z9nlRwAQU4ERERERE5r1dp3ZNSyC51HyyrsCY55PMJ3MZFzmpOfEAtqRwSTyADW/LTY89T8vD6/ZOx1twVbat3DZvAttYCnAiIiIiIvPY5criTzSfbFw4i/WeXcl8svz0/HhPWUIPWex5TmoObpd7Nt+C64qx1ia7DQmqqqrs3r17k90MEREREZE5bSgyRGt/Kw/9x0N0BjvHve42bnxe32Xnk11qyOJwAZBMb+aE88lkZhhj9llrqyZ6TT1wIiIiIiJzTCgSoi3QRkt/S/yjdaA14XFXsOuS54jYCG9e+eZx4Wy46Md0zCeT2acAJyIiIiIyi0LREB0DHbQMxMJYf+u4x52BTiyJI+UyvZkUZRThz/BzU95N+DP8+DP8PLnvyQnDXHFGMZ+o/cRs3ZbMEgU4EREREZFpEolGaA+0T9hj1trvPO8IdoybZ5bhzcDvcwLZmtw18cdFPiewFWUUkeHNmPCaXpd3XpfFl6ujACciIiIicgUi0Qidwc4Jw9nw445ABxEbSTgu3ZMeD2OvKX1NQjDz+5xwlpmSec3tmu9l8eXqqIiJiIiIiFz3ojZKV7Ar3kvWMtCS8Lilv4X2gXbCNrFsfpo7LR7Ihoc3xsNZ7PFkC0qLTEZFTERERETkumWtdcLZJL1mrQOttA20EYqGEo5LcaXEQ1lVUdWE4Sw7NVvhTGaVApyIiIiIzDlXujC1tZaewZ4JC4KMnns2FB1KOM7j8sTDWGVh5bghjf4MP4tTFyucyZyjIZQiIiIiMqeMXZganEId21Zso8BXMFIQJDbMcfR+AB7jodBXGC/+MTqUDT/OTcvFZVyzfWsiV2TGhlAaY+4BdgJu4CvW2s+MeX0p8DUgJ7bPx6y1z03lmiIiIiKyMA1GBjnSeYRP7/70uFAWiob47svfxW3cFPgK8Pv83Jh7I3eU3ZEQ1PwZfvLS8xTOZMG65gBnjHEDXwLeAJwD6owxz1prj4za7U+BZ6y1XzbG3AQ8ByyfQntFREREZAGw1tJ0sYnG9kYa2htobG/k2IVjhKPhSY8xGPa9ax9ul3sWWyoyt0ylB64GOGmtPQVgjPkmcB8wOsBZICv2OBs4P4XriYiIiMg8NRAa4HDn4XhYa2xvpDPYCThl9jfkb+A9N72HioIKPrX7U7QOtI47hz/Dr/Am172pBLhS4Oyo5+eA2jH77AB+aIz5EJABvH6iExljHgEeAVi6dOkUmiQiIiIiyWat5dW+V+NhraG9gZcuvBRfH2151nJeW/paKgoqKC8oZ3XOajyukV9LB8IDWphaZBIzXYXyQeDvrbWfM8bcAnzdGLPB2sSl5621TwNPg1PEZIbbJCIiIiLT6OLQRQ52HBwZDtnRSM9gDwAZ3gw25m/kdzb+DuUF5ZTnl5OTlnPJ82lhapHJTSXANQFLRj0vi20b7X3APQDW2heMMWlAPtA2heuKiIiISJJEbZRXel5J6F17uftlLM7f4Fdlr2Lr0q2U55dTUVDBiuwV1zTscdvKbQpsIhOYSoCrA24wxqzACW4PAO8Ys8+rwFbg740x64A0oH0K1xQRERGRWdQz2OPMWetw5q0dbD9IX6gPgKyULMoLyrl7+d1UFFSwIX8DWSlZlzmjiEzFNQc4a23YGPMo8AOcJQK+aq09bIz5JLDXWvss8BHgb4wxf4BT0ORhO9cWnhMRERERACLRCCe7Tyb0rp3uPQ2Ay7i4IecG7llxT3zu2rKsZSrXLzLLtJC3iIiIyHWqK9gVrwjZ0N7AoY5DDIQHAMhNy3WGQRZWUJ5fzvr89WR4M5LcYpHrw4wt5C0iIiIi80MoGuLEhRM0tDXEh0Oe7XMKinuMhzW5a7hv9X2UFzhz18oWlWGMSXKrRWQsBTgRERGRBah9oD1hKOSRziPxsvwF6QVUFFTw9jVvp6KggnV560j3pCe5xSJyJRTgREREROa5ocgQR7uOJgyHbO5vBsDr8rIubx1vW/M2KgorqMivwJ/hV++ayDylACciIiIyj1hraelvoaGjIT4c8mjnUULREADFGcWUF5Tz0E0PUV5QzrrcdaS4U5LcahGZLgpwIiIiInNYMBzkSOeR+HDIxvZG2gLOkrqp7lTW563nXeve5SySXVBOoa8wyS0WkZmkACciIiIyC3ad2sXO/Ttp6W/Bn+Fn++bt4xaqttZy7uK5hLB2vOs4YRsGYEnmEqqLq+PVIdcsXoPX5U3G7YhIkijAiYiIiMywXad2seP5HfEiIs39zex4fgeD4UHKMsto7GiMh7auYBcA6Z50NuZv5OEND1NRUMHG/I3kpecl8zZEZA5QgBMRERGZYTv374yHt2HBSJDHX3g8/nx51nJeV/o6KgoqqCioYFXOKjwu/aomIon0U0FERERkBlhrOdN7hrrWunhFyIk8tfUpygvKyU7NnsXWich8pQAnIiIiMg2stbzS8wp1LXXsbd3L3ta9dAQ6AHAZF1EbHXdMcUYxt5bdOttNFZF5TAFORERE5BpEbZSXu19mb+te6lrq2Ne6Lz5/rdBXSI2/hip/FdVF1RzqOMQTLzyRMIwyzZ3G9s3bk9V8EZmnFOBERERErkDURnnpwktO71qL08PWPdgNgD/Dz2tLXhsPbGWZZQkLZS/PXo4x5rJVKEVELkcBTkRERGQCkWiE4xeOx8PavtZ99A71AlC6qJTby26nyl9FVVEVpYtKEwLbRLat3KbAJiJTpgAnIiIiAoSjYY53HY/PYdvfup++UB/grL+2delWqv3VVBVVUbyoOMmtFZHrlQKciIiIXJdC0RBHO4/G57AdaDtAf6gfcEr637387ngPmz/Dn+TWiog4FOBERETkuhCKhDjceTg+h21/234C4QAAK7NXsm3FtnhgK/AVJLm1IiITU4ATERGRBWkoMsTBjoPxOWwN7Q3xwLY6ZzX3rbqPKn8VNxfdTH56fpJbKyJyZRTgREREZEEYjAzS2N6YENgGI4MArFm8hrfe8FaqiqrYXLSZ3LTcJLdWROTaKMCJiIjIvBQIB5zAFpvDdrD9IEPRIQyGG3Nv5O1r3k61v5qbi24mOzU72c0VEZkWCnAiIiIyLwyEBqhvr2dvi1PSv7GjkXA0jMu4WJe7jgdvfJAqv9PDlpWSlezmiojMiCkFOGPMPcBOwA18xVr7mTGvPwncGXvqAwqttTlTuaaIiIhcH/pD/RxoOxAfEnm44zBhG8Zt3KzPW89DNz1EVVEVmwo3kZmSmezmiojMimsOcMYYN/Al4A3AOaDOGPOstfbI8D7W2j8Ytf+HgE1TaKuIiIgsYH1DfQmB7UjnESI2gsd4WJ+/noc3PExVURWVhZVkeDOS3VwRkaSYSg9cDXDSWnsKwBjzTeA+4Mgk+z8IPD6F64mIiMgC0jvUy/7W/fGFs491HSNqo3hcHsrzy3nfxvdRVVRFRUEFPq8v2c0VEZkTphLgSoGzo56fA2on2tEYswxYAfx0ktcfAR4BWLp06RSaJCIiInNVz2BPfA22va17Od51HIslxZVCeUE5j5Q/QnVRNeUF5aR50pLdXBGROWm2ipg8AHzLWhuZ6EVr7dPA0wBVVVV2ltokIiIiU7Dr1C527t9JS38L/gw/2zdvZ9vKbfHXu4Jd7GvdFw9sJy6cACDVnUplQSUfrPwgVUVVlBeUk+pOTdZtiIjMK1MJcE3AklHPy2LbJvIA8D+mcC0RERGZQ3ad2sWO53cQjAQBaO5v5vHnH6ehvYGojbKvdR8nu08CkO5Jp7Kgkg9t+hBVRVVsyN9Aijslmc0XEZm3phLg6oAbjDErcILbA8A7xu5kjLkRWAy8MIVriYiIyByyc//OeHgbNhgZ5BvHvoHP42NT4Sa2rdxGVVEV6/PW43V7k9RSEZGF5ZoDnLU2bIx5FPgBzjICX7XWHjbGfBLYa619NrbrA8A3rbUaGikiIjKPdQe72du6l93Nu2nub55wH4PhVw/+Co9LS82KiMyEKf10tdY+Bzw3ZttjY57vmMo1REREJDn6hvrY17qP3c27qWup48SFE1gs6Z50Ut2pDEYGxx3jz/ArvImIzCD9hBUREREABkID7G/bz57mPexp2cPRrqNEbTRedOTRTY9S469hff56fnj6hwlz4ADS3Gls37w9iXcgIrLwKcCJiIhcp4LhIPXt9fHAdrjjMGEbjq/D9kj5I9T4ayasEjlcbfJSVShFRGT6mbk2Na2qqsru3bs32c0QERFZcIYiQzS2N1LXUsfult00tjcSioZwGzfr89dT46+hxl9DZWEl6Z70ZDdXROS6ZYzZZ62tmug19cCJiIgsUKFoiMMdh+OBraGtgWAkiMGwLm8d71z3Tqr91dxcdDMZ3oxkN1dEZPY0PgM/+ST0nIPsMtj6GJTfn+xWXREFOBERkQUiEo1wrOsYe1qcIZH7W/czEB4AYM3iNbxtzdvigS07NTvJrRURSZLGZ+B7H4ZQwHnec9Z5DvMixCnAiYiIzFNRG+WlCy/FA9u+ln30hfoAWJG9gntX3UuNv4YqfxW5ablJbq2ISBJEI9DfDn3N0NsMfefhx0+MhLdhoYDTI6cAJyIiItPFWsupnlPsadlDXUsddS11dA92A7Akcwl3L7+bGn8N1f5qCnwFSW6tiMgMC/ZCX4sTynqbnZDW1wy952OPW5wPG7my8/Wcm9n2ThMFOBERkTnKWsurfa86ga25jj0te+gMdgJQnFHM7WW3U1PsFB7xZ/iT3FoRkWkSCcHF1pEes76WUaGseSSsDV0cf2xqNmQVQ2Yx5K8deZxVApl+yCyBv339xGEtu2zm720aKMCJiIjMIecvno8vnL2nZQ+tA60AFKQXUFtcS21xLdX+asoWlWGMSXJrRUSugrUQuJAYwuI9ZqN60vrbgTGV8l3eWBArhqKbYPXrR8LZ6ICWcgUFmbY+njgHDsCb7hQymQcU4ERERJKotb81PiRyT8semi42AZCblktVUZVT2r+4huVZyxXYRGTuCgVHAtm4gDaqJy0cHH+sL8/pGcv0Q3HFyOOskpGA5ssDl2t62jo8z01VKEVERORyOgOd8bBW11LH6d7TAGSlZFFVVMVDNz1Ejb+GVTmrcJlp+mVFRORaRaMw0JEYxMaFs2YIdI0/1pM20jtWWjVxj1lmMXhSZ/++yu+fN4FtLAU4ERGRGdQz2MPelr3sbnGGRZ7sPglAhjeDm4tujpf2X7t4LW6XO8mtFZF561rWNRu8OKYIyNj5ZrEiINHQmAMNLCp0wtfiZbC01uk1SwhoxZCWAxo5MO0U4ERERKZR31Af+1v3xwPb8a7jWCxp7jQ2F21m28pt1PhruCnvJjwu/TcsItNgonXNnv0QdL3izBebcL5ZMwz2jj9XSuZIEFv22tjjMUMaFxWBWz+/ksVYay+/1yyqqqqye/fuTXYzRERErshAaIADbQecwNZcx5GuI0RtlBRXChWFFc4cNn8NG/M34nV7k91cEVlowoPw5Abob7v0fi4PLPLHApl/gh6zWEhLzZydds8BobY2mv7wI5Q9+Xk8BXNr6RVjzD5rbdVEryk6i4iIXIVgOEhDe4OzeHbzHg51HCJsw3iMh40FG3n/xvdT46+horCCVHcS5nWIyMIW6Iaze+DVF5yPpv0QGZxkZwOP/MwJaBkF01cEZIHoeOrLBPbto/2pL1P8+PyoQAkKcCIiInG7Tu1i5/6dtPS34M/ws33zdu5edjeNHY3xwNbY3shQdAiXcbE+bz3vXv9uav21VBZW4vP6kn0LIrLQ9DSNhLVXX4TWw4B1etSKK6Dm/dDwTafQyFjZZVBSOetNTqZoMEikp5dobw+Rvj4iPT1Ee3uJ9PQS6e0l2uc8DrW3M/D882AtPd/+NgW/98E51ws3GQ2hFBERwQlvO57fQTAyUuLahQuXcRG2YQyGG3NvpNpfTW1xLZsLN7MoZVESWywiC040Ch3HnbB2JhbYel51XktZBGXVsOw1sHQLlN48subZ2Dlw4Kxrdu8X5l2lRWst0f4BJ4D1xkLXqAAW6e0h2ts38nh4e18v0Z5e7NDQJc/vysjAlZ1FdCBAtKfHWZvO4yHn7W+fU71wGkIpIiIyiXA0zNHOo3xq96cSwhtAlChp7jQ++7rPUuWvIjs1O0mtFJEFKTwI5+tHetfOvugsdA2QUQjLboFbfg+W3gJFGyYvHDLH1jWz0SjRvr4JAlisN6y3b+Rxz6h9enuJ9PVBODz5yY3BlZWFOysLd2YmruwsUouKnOfZWbgync/urCxcWdmjHjv7G4+HUFsbL7/hbie8AYTD9HznO/OmF04BTkRErivhaJhjXceoa6mjrqWO/W376Q/1T7p/IBxg67Kts9hCEVmwgj1wtg5efd4JbE37Rha2zlsNN77ZCWtLt0DuyqsrwV9+PyH/HdNWlMOGQkT6+kaC1egA1hPr8ZqkZyza1zcSjibi8eDOzHSCVXY27uxsUpYswZWdhTsWwJyQlh0LZZm4s7Od/RctwkxxLl/HU1/GRqOJ9xuNzpu5cApwIiKyoEWiEY5dOEZdcx11rXXsb93PxdBFAJZnLWfbim1U+6v57N7P0jrQOu54f4Z/tpssIgtF7/mR3rUzL0DrIcCCcTvz16re5/SyLdkCi6be8zO2KEd0cPDSASwevnqJ9sTmjMUeRwcGLnktk5LiBK4sJ1h5CwpxrVo90hM2SQBzZ2VhfD5MEteHC9TXQ2jM2nahEIEDB5LToKukACciIgtKJBrh+IXj8R62fa37EgLbPSvuocZfQ1VRFQW+kV+YIjYybg5cmjuN7Zu3z/o9iMg8ZC10nIAzsd61V1+A7jPOa16fM3/tjo/F5q9VQeq1z0fTnCcAACAASURBVKG10Sjh9nZCTU2Ems4Tampi8ORJep97Dqyl+xvfoPvb34bLzAczPl88VLmzsvCWlpK2bl1iAMvKdB4P94DFHrtS52+V3ZXf/bdkN2FKphTgjDH3ADsBN/AVa+1nJtjnfmAHYIEGa+07pnJNERGR0SLRCCcunEgIbH2hPgCWZS3jjcvf6AQ2fxWFvsJJz7Nt5TaAcVUoh7eLiCQID0FzQ2KFyECX81pGgRPUaj/gfPZvhKtYB9JGIoRbWwmdd8LZUFNT/HGo6Tzh5mbs2B6k1FSnCAqAy0XqypVk3XMPrqzMeE9YPIANzwdLSZmmN0Nm0zVXoTTGuIETwBuAc0Ad8KC19siofW4AngHustZeMMYUWmsvucqgqlCKiMilRG10XGDrHeoFYGnmUqr91VT5q6guqqYooyjJrRWRBSPYC+fqRsLaub0QjlV9zF01Mndt2WsuO3/NhkKEWlvjvWehhIDWRKi1dVwhD3dBPt6SElJKS/GWluItKXE+l5ZiPB5O/fp92MGR9eBMaiqrf/yjeVGUQ8abqSqUNcBJa+2p2EW+CdwHHBm1z/uBL1lrLwBcLryJiIiMFbVRXrrwUjyw7W3dGw9sSzKX8Pplr6eqqIpqf7Xmq4nI9OlrGTV/7Xln/pqNgnGBvxyq3usEtiVbIDPxj0V2aIhQS8u4cDbckxZuaR3pLQMwBk9hId6SEtI3bSJrVDjzlpTgLSnGlZY2aVObdzwxr4tyyNWZSoArBc6Oen4OqB2zzxoAY8yvcIZZ7rDW/ufYExljHgEeAVi6dOkUmiQiIvNd1EY52X0yIbD1DPYAULaojK1Ltzq9bEVVFC8qTnJrRWRBsBY6TybOX7vwivOa1wdlVXDbR53AVlZNlJRYKDtP6NDPR3rSYkEt3NaWWIXR5cJTVIS3tISM6mo8Y3rSPMXFuKYwnHG+F+WQqzPTRUw8wA3AHUAZ8AtjzEZrbffonay1TwNPgzOEcobbJCIic0jURnm5++WEwNY96Pw3UbqolDuX3BkPbCWLSpLcWhFZECIhaG4cNX/tBRjodF7z5RH11xIqfSshVxmhYCqh5hZCDU2Emp5m6PzjRNo7Es/nduP1+/GWlpJxyy2JvWdlpXiLijDeK58Dd7Xme1EOuTpTCXBNwJJRz8ti20Y7B+y21oaAV4wxJ3ACXd0UrisiIvOYtdYJbK2xwNaylwuDzsK1xRnF3FZ2W7zoSOmi0iS3VkQWhMGLcG5PvHct8speQt1DhAbchGwRIVYTGqog1Bsh1NpJpKseqB853uvFW1yMt7SERbfdhre01OlBiw119BQWYjwq7i6zYyrfaXXADcaYFTjB7QFgbIXJ7wIPAn9njMnHGVJ5agrXFBGRecZay6meUwk9bF1Bp1KbP8PPrWW3UlVURU1xjQKbiCQItbVd08LUkZZThPb9gNCRFwm9fIRQcyuhfhehfg+hQAqRYM6ovcOYlNZ4GEvbUBnrQRuZh+bJz8e43dN/gyLX4JoDnLU2bIx5FPgBzvy2r1prDxtjPgnstdY+G3vtbmPMESACfNRa2zkdDRcRkbnJWssrPa84gS3WyzYc2Ip8Rby25LXxSpFli8qSupiriMxtYxemBudnTLSnh9D5805RkHNNhE4dJXTqmDMHresi0cHE8xhvFl5/Ad6bVpC2ZGl8eOPwPDR3Xh7G5UrCHYpcvWteRmCmaBkBEZH5xVrL6d7T8R62upY6OoPO3+oKfYXU+Guo9ldTXVRNWaYCm4hcmcH/fIpTf/jXEAUM+DasJDLoIdTURLS/P2FflyeKNyOCN9OFt7gI7/Ib8K69Ge/6W/AuXYY7N1c/e2RemallBERE5DpkreVM7xmnd63Z6WXrCDgT+gvTC9lSsoXqomqq/dUsyVyiX5pE5LJsNMrQqVME6usJNDQQePEXDJ5tBWI/P6wlePwlfKWp+Jb0400LOoHNX4h3XQ3uG1+HWfYayLsB1JMmC5wCnIiIXJK1llf7Xh2Zw9ayl7aAs6xnQXoB1f7qeC/b0sylCmzXk8Zn4CefhJ5zkF0GWx+D8vuT3aq5az69X9ZCZAjCg6M+D0J4aMznQaei47htlz420tdP4NULBM70Ejh3kcD5ANFBZ1SYOxVScgbBpEB8oJjBRlwUV7bgufXhkUWzs1SZVq4/CnAiIpLAWsvZvrMJc9jaBpzAlp+e7/SuFTtDIpdlLVNgu141PgPf+zCEAs7znrPOc5i7oSSZLvV+bXz7zIWlSOgS55vkHMMf08RaF4MXfQS6Ugl0eAm0uxgaXlDKQGqBl6ybMkkvyyB9WTYpBZm0fP2/CLSPDnBOpmw/mE7xX/zVtLVNZD5SgBMRuc5ZaznXdy4e1upa6mgdaAUgLy3Pmb8W+1ietVyBTRw/+eRIGBkWCsB/fgw8qWCjsQ876vFkH9Oxz2ycw17BvpNs7z4N0cj49+s773c+potxOV1YnhTnsztl5HH8cyp4c5zP7pQxn4f3S5lg25hzjDvWOSbcO0DgyEsEDh0jcPAwgUOHsAMDALhzc0nfXEl2ZSXpFRWkb1iPKyNj3G0EvrDOmfs2WtQQuDB+X5HrjQKciMgCtuvULnbu30lLfwv+DD/bN2/n11b8Gk0Xm0aKjrTW0dLfAkBuWm684Ei1v5oV2SsU2CRRNApN+5wepIkMdMIz757ZNhjXJT7M9LzOJfZzea7+Gl0vT34/t//xlYWlhDA2Sfhyz+6vdjYUInj8xMjctfp6Qmdj3xseD2k33kjOW9/qhLXKCrxlV1bIaOWXn0jssQTwpsO9X5ihOxGZP1SFUkRkgdp1ahc7nt9BMBKMb3MbN4u8i+gZ6gGcwFZVVBXvYVuZvVKBTcYLD8Erv4Bj34fjz8HF1sn3XVQED/3bNQSnq9hnPnpyw8ShN3sJ/MGh2W/PNQq1thFoGA5rDQQPHcIOOjX7PYWFpA/3rFVWkLZ+Pa60tGu/2HyaMygyzVSFUkTkOjIUGeJo11E+vfvTCeENIGIjDEYG+UTtJ6guqmZVzioFNplYsBdO/giO7YKXfgSDveDNgBteDze+GYb64QcfH99DcvefQ9H65LV7rtr62MQ9SlsfS16bLiM6NMTgkSMEGhoYiPWwhc83A2C8XtLWr2fxAw+QXllBemUlHr9/en+elN+vwCYyAQU4EZF5zFpLS38LDe0NNLQ30NjRyNHOo4SioUmPGYwM8uCND85iK2Xe6GtxetiO7YJTP4doCHz5sP43nNC24nbwjupRSclQD8mVGn5f5uj7Za0l3Nw8aihkA8EjR7Ah52eJt6QEX2Ul6e95D+mVlaSuW4crJSXJrRa5PmkIpYjIPBIMBznceZjG9kYa2xtpaG+gPdAOQKo7lfV566koqKC8oJy/2PMX8eqRoxVnFPPDt/1wtpsuc1XHSTj2PSe0natzti1eAeve7IS2smpwuZPbRpl20WCQ4KFD8bAWqK8n3O78LDFpaaRtWI+vspK0igrSyyvwFhUmucUi1xcNoRQRmYeGq0M2dDTEw9qJrhOEbRiAJZlLqCmuoTy/nIrCCtYsXoPX5Y0fPxgZHDcHLs2dxvbN22f9XmQOiUbh/AFnPtuxXdBx3NleXAl3/incuA0K183fuWYyjrWW0NmzCWEtePw4hJ2fJd6lS/HdssWZu1ZRSdraNRiv9zJnFZFkUYATEZkjBkIDHOo4RGNHIw1tznDIrmAXAOmedDbmb+ThDQ9TUVDBxvyN5KXnXfJ821ZuAxhXhXJ4u1xHwkNw+r+dwHb8OehrBuOG5a+D6t+BtW+CnCXJbqVMk2h/P4GDh0aGQzY0EOlyfpa4fD7SysvJe9/74sVGPLm5SW6xiFwNDaEUEUmCqI1ypveMM28tNhzype6XiFpn4aPlWcvjQyErCipYlbMKj0t/c5OrMNgHJ3/shLYTP4TBHvD6YPVWZ2jkDXeDT7+4z3c2GmXo9JmEsDZ44oTT0wqkrFwZ61mrIH1TJamrV2PcGhIrMtdpCKWISJL1DfVxsP1gfDhkY3sjvUO9AGR6M9lYsJE7l95JeX455QXlZKdmJ7nFMi9dbBtVhORnEBkCXx7cdK8T2lbe4VQ+lHkr0tdHoKFxpJR/QyPRHmdZEFdmJunl5WR+4AOkb6okfeNG3Dk5SW6xiEw3BTgRkWkWtVFe7n7ZCWqx4ZCnek5hsRgMq3JW8YZlb4j3rq3IXoHLuJLdbJmvOl8emc92dg9gIWcZ1DzizGdbUqsiJPOUjUYZPHkyvkB2oKGBoZdPgbVgDKmrV5N19xvia6+lrFyJcelnichCpwAnIjJF3cFuJ6jFhkMe6jjExdBFALJTsynPL+eeFfdQUVDBhvwNZKZkJrnFMq9ZGytCssv5aD/qbPeXwx0fd0Jb0XoVIZmjQm1tNP3hRyh78vN4CgoSXgtfuECwsZGB+nqCDQ0EGg8Svej8LHHn5JBeUUH2tm2kV1SQVl6Oe9GiZNyCiCSZApyIyFUIR8Oc7D4ZLzLS2N7I6d7TALiMizWL17Bt5TbKC8opzy9nWdYyLZQtUxcJwelfjhQh6W1yipAsew3c/Jdw469BztJkt1KuQMdTXyawbx/tX/wSi3/r/oTKkENnzjg7ud2krl1D1r1vJr2iAl9lJd5l+lkiIg4VMRERuYSOQEd8zlpjh9O7FggHAMhNy00oNLI+bz0+ry/JLZYFY/AivPwTOPp9eOkHEOwBT3qsCMk2WHOPipDMcdZaIhcuEGpqItR0nuDx43Q+/TREIgn7ufPy4sMg0ysrSN+wAZdPP0tErmcqYiIicgVCkRDHLxyPD4VsaG+g6WITAB7j4cbcG3nL6rfEA1vpolL9RVym18V2OPEfTk/by/8FkUFIz3UKkNy4DVbeCSn6xX6usNYS6ehwAtr58ww1NcUfh5rOEzp/HhsITHywy4VvyxaKP/kE3lL9LBGRK6cAJyLXrdb+1oQ11450HmEwMghAYXohFYUVPLD2ASoKK1iXu440T1qSWywLUtepkflsr74IWMheClW/DeveDEu2gFv/XSeDjUYJt7fHe9BGwlksqDU3YwcHE45xZ2fjLS0ldeVKFr3udXhLS/GWlWLS0jj3wd8b2T8aJbBvH67UVIU3Ebkq+h9BRK4Lg5FBjnYeTehdax1oBcDr8nJT3k3cv/Z+KgoqqCiowJ/hT3KLZcGyFpobYqHt+9B2xNletBFu/2Onp82/UUVIZoGNRAi3tsZD2VBCQDtPuLkZGwolHOPOzXUC2tq1LLrrLrylJXhLSpygVlKKe1HGhNdq3vEENrY2W/z60SjtT32Z4scfm7F7FJGFZ0oBzhhzD7ATcANfsdZ+ZszrDwN/BTTFNn3RWvuVqVxTRORyrLU09zcnhLWjXUcJR8MAlGSUsLlws1NopKCcG3NvJMWdkuRWy4IWCcGZ50d62nrPgXHB0tfAG//CKUKyeHmyW7ng2FCIUGvbSI/Z6N6z8+cJtbRAOJxwjLsgH29JCekb1uN9490j4ay0FG9x8TXPTQvU18OYMEgoRODAgWu9PRG5Tl1zERNjjBs4AbwBOAfUAQ9aa4+M2udhoMpa++iVnldFTETkUnad2sXO/Ttp6W/Bn+Fn++bt3LX0Lg53HE4YDtkR6AAgzZ3G+vz1zry1fKfgSIGv4DJXEZkGQ/1w8idOYDvxnxDsBk8arLorVoTkTZCRl+xWzmt2aIhQS8u4gDbckxZuaYXRvV7G4CksTAxl8cdOT5orNTV5NyQiEjNTRUxqgJPW2lOxi3wTuA84csmjRESu0a5Tu9jx/A6CkSAAzf3NfPy/Pw6Axflj1NLMpWwp3hIvNHLD4hvwurxJa7NcZ/o7nLB2bBe8/FMIByEtB9a+yQltq+6ClImH2Ml40cHBUQVBxs9DC7e1OUNSh7lceIqK8JaWkFFdjaekhJRRQc1TXIwrRb3tIjK/TSXAlQJnRz0/B9ROsN9vGmNuw+mt+wNr7dmxOxhjHgEeAVi6VOvYiMiI4blr9W31fLH+i/EiI8MslkXeRXzm1s+wsWAjuWkqq56g8Rn4ySeh5xxkl8HWx6D8/mS3amG5cHpUEZIXwEYhewnc/LAT2pbeAm79EWEi0UBgZFjj6OGNTecZOt9EpL0j8QC3G6/fj7e0lIxbbknsRSsrxVtUhPHqvRaRhW2mi5h8D/iGtXbQGPO7wNeAu8buZK19GnganCGUM9wmEZnDOgIdNLQ1UN9eT31bPYc7DxOKhi55TH+on9uX3D5LLZxHGp+B730YQrEy5j1nneegEDcV1kLLwZHQ1nrQ2V64Hm79Iye0FVdcN0VIQm1tNP3hRyh78vN4ChKHJ0cu9js9Z6MKg4wOapGursSTeb14i4vxlpaw6Lbb8JaWOj1osWGOnsJCjEf110Tk+jaVn4JNwJJRz8sYKVYCgLW2c9TTrwD/ewrXE5EFJhKN8HLPy9S3OWGtvr2es31OJ73X5WV93nreue6dVBZWUlFQwTt2vYPm/uZx51HFyEn85ImR8DYsFIDnPgqDveBOBU8quFPGfE51eozGbvOkxF5LAZcrOfc00ybrsYyEnd614dDW8ypgnN61uz/lFCHJXZns1idF2199lsC+fZzb/vukl5cnzEeLdHcn7GtSUpwwVlJC2tatI3PPYj1pnvx8jNudpDsREZkfplLExIMzLHIrTnCrA95hrT08ap9ia21z7PFbgD+21m651HlVxERk4eoP9XOw4yAH2g7Q0NZAQ3sDF0MXAchNy2VT4SYqCyqpLKzkprybxlWGHDsHDpwiJTtes4NtK7fN6r3MSdZC50k49TPn49j3Z+5arrEBL2WCIDhBMBwOgaPD4Nhtlzs2Hi7HHDPVHq+xPZbgXL/kZug4BoELzrVW3eksrL3mHlh0/RXECbW0MLB7N/2799D//POEW1pGXkxNHZlzVlqCt8T5nFJaiqekxAloCzX8i4hMoxkpYmKtDRtjHgV+gLOMwFettYeNMZ8E9lprnwU+bIz5dSAMdAEPX+v1RGR+sdZyvv98Qu/aiQsniNooBsPqxat504o3xUNbWWbZZRezHQ5pY6tQXtfhrfc8nPo5vPJz53PfeWd79lLwZkCof/wxWaXw/v+CyJDzER6EyCCEh8Z8Hhz1+qjP47ZNcmw4CMEep4T+ZOe1kel7LyYKlQlBb6KQOSoENvzz+B7LyBCcfdHphbtxG6zaCqmLpq/N80C4o4P+3bsZ2L2Hgd27GTpzBnAWrHZlLgK3GyIR8HrJeetbKH788SS3WERkYbvmHriZoh44kfkpFAlxrOsYB9oOUN9eT0NbA22BNgB8Hh8bCzbGw1p5QTmZKZlJbvE8FeiG0790ethe+Tl0nHC2p+fCytthxe3O58Ur4OC/ju9R8qbDvV+YO3PgopHxITASuspQOfqYqwikkaHE14LdkzTSwI7JXlt4whcuMLCnzull27OboZMvA+BatAhfVRW+LbVk1Nbizs3l5bvfiB0cKSxkUlNZ/eMfjZsLJyIiV2emlhEQketYd7A7XmjkQNsBDncejleILF1USpW/isrCSjYVbmJ1zmo8Lv24uSahAJzdHRsW+XNorneqHHp9sOw1sPndTmgr2jB+XtpwSJvLVShdbkjxAde2OPK0enKDU+hlrOyy2W/LLIr09jKwd298WOTgsWMAGJ8P3+bN5PzGb+CrrSVt3bqEAiLNO57Ajl5jDbDRKO1PfZnixx+b1XsQEbme6DcqEbmsqI1yuuc09e1OWKtvq+d072kAPMbDurx13L/2/vj8tUJfYXIbPJ9FI3C+Hl75mRPaXt3t9BC5PFBaBbf9T6eHrbTKGfp3OeX3z63ANpdtfWziHsutCyuMRPv7Gdi/n/4XX2Rg9x6CR45ANIpJTSV90yYKtn8YX+0W0jduuGRJ/kB9PYTGVIgNhQgcODDDdyAicn3TEEoRGScQDnCo41B87lpDewM9gz0A5KTmUFlQSUVhBZsKN7E+bz1pnrQkt3ges9YZBjncw3b6lxB7rynaACvvcHrYlt0CqRp2OuMW4Lp50WCQwIEDzjy2F3cTOHQIwmHwekmvKCejphbfllrSKypwpaYmu7kiIsKlh1AqwIkILf0t8eGQ9W31HO86TtiGAViZvZLKwsp479ryrOWXLTYil9HTFCs68jMntF2MVfHLWeYEtpW3w/LbrssKhzJ10aEhgg0N9O/ew8CLLxJoaMCGQuB2k75hA77aWjK21JK+aROu9PRkN1dEJCm+e6CJv/rBcc53ByjJSeejb1zLb2wqTXaz4jQHTkTiwtEwJy6ciJfyr2+vj6+tluZOY2PBRt674b3xtdeyU7OT3OIFYKArsfBI50lnuy8fVtw2EtoWL09eG2XesqEQgUOHGNi9h/7dLxI4UI8NBsEY0m66icUPPURGbQ3pN1fhXpSR7OaKiCTddw808fHvHCQQciohN3UH+Ph3DgLMqRA3GQU4kQWud6g3HtQa2hpo7GgkEHbm+BT6CtlUuIn3rH8PlQWVrMldg9c1+ZwXuUJDA07p+VOxXrbmBsA6Zf2XvxaqftsZFll408JdEFtmjI1ECB45ysCe3fTv3k1g7z6iAwMApK5dS879byejthZfVRXubP0BRkSuX9ZaegNhznUP0HQhwPnuAE3dAb7+4hmCocQiTIFQhL/6wXEFOBGZXdZaXu17NV4ZsqG9gZPdTm+P27hZs3gNb1n9lviQyOJFxUlu8QIRCcP5AyM9bGd3OyXqXV4oq4Y7Pub0spXe7CxCLXIVbDTK4IkT8SqRA3V1RPv6AEhZtYrs37gPX00tvppqPLm5SW6tiMjsiUQtbX1Bmi44waypOxB/fD72uH8ocb3RVI+LwXB0wvOd7w5MuH2uUYATmccGI4Mc6TwSrwzZ0N5AV7ALgMyUTCoKKrhn+T1sKtzEhvwN+LxzoFT7QmAttB8b6WE78ysY7HVe82+Emkdg5Z1O4ZEUDVmTq2OtZejUqXiVyIE9e4h0O+vQeZcuJeuee/DVOoHNW6iKryKycAVDkXgoOz8moDV1B2jpCRKOJtbzWOzzUpKTzvK8DF67Op/SnHTnY3E6JTnp5GWk8Lq//C+aJghrJTnzY16wApzIPNIR6IgXGjnQfoAjnUcIR51iI8uylnFr6a3x3rWVOStxGQ3PmzbdZ0cKj7zyC7jY6mxfvAI2vNXpYVt+G2TkJbGRMh9Zawm9+ir9L+52etnq9hBp7wDAU1LMojvvxFdbQ0ZtLd5i9ZqLyMJgraV7IDRxz1nseWf/UMIxLgP+rDRKF6dTtWwxJbFgNhzSSnLSyUi9fLz56BvXJsyBA0j3uvnoG9dO+33OBAU4kSTbdWoXO/fvpKW/BX+Gn+2bt7Nt5TYi0Qgnu0/GS/nXt9Vz7uI5AFJcKWzI38BDNz0Urw6Zm6ahU9NqoMsJasPDIrtOOdszCpz5ayvvcAqP5CxNYiNlvgo1NTnDIXe/SP/uPYRbnEqknoICMmq3kLGlFl9tLd6yMlV9FZF5KRyJ0to3GB/KOFFQGxgzvDHN64r1lvlYX5I10nOW7Xz2Z6XhcU/9j9PD89zmchXKS9EyAiJJtOvULnY8v4NgJBjf5jEelmctp3mgmf5QPwB5aXlsKtzk9K4VVrIudx0p7itYxFmu3FA/vPrCSGn/loOAhZRMp/DIcGgrXAf6hVquUqi1zSk6EhsWGTrn/DHGnZuLr6bGCWw1taSs0DIdIjI/BIYiCaFs7BDHlt4gkTHDG3MzUmI9ZWmU5vhivWcjjxf7vPoZGKNlBETmqJ37dyaEN4CwDXO67zS/ecNvUlHgLJZduqhUP9CmWyQETftHhkWe3QPRkFN4ZEkt3PkJJ7CVbFLhEblq4c5OBvbsiQ+LHDp9GgBXdja+6ipy3/1ufFtqSb3hBv3bFpE5x1rLhYFQLIwN0NQdjD8+3x2kqTtA15jhjW6XcYY35qRTsyI3PqRxeIhjSU4avhRFj+mgd1EkSY51HYuvvzZWJBrhT7f86Sy3aIGzFtqOJBYeGboIGCguhy0fdIZELlXhEbl6ke5u+uvqnKIju19k8CWn+qsrIwNfVRU5999PxpZaUteuxbjdSW6tiCxEV7MwdTgSpaU3OG7e2blYT9r57mDC/DBw5ogNh7GNZdnjioMUZaZOy/BGuTwFOJFZ1trfyl8f+GuefflZDAbL+GHM/gx/Elq2AF04k1h4pL/d2Z67CsrvjxUeuRV8mj8oVyfS18fA3r2xxbN3M3jsGFiLSU/Ht3kzWff+Ohlbakm76SaMR//VisjMmmhh6j/+diNHmntYlpcxrrR+S2+QMaMbyctIoXRxOmuKMrljbWE8nA0HtRwNb5wz9L+KyCwZCA3w1UNf5WuHv0bERnjP+vewLGsZf7nnLxOGUaa509i+eXsSWzrHNT4DP/kk9JyD7DLY+pgTxgD6OxILj1w47WxfVOSU9V95uzOXLWdJslov80CorY2mP/wIZU9+Hk9BAQDRgQEG9u2PLZ69h+ChQxCNYlJSSN+0ifwPPUpGbS3pGzdiUjQ/VURm3oX+IU539nO6s5/H/v3wuB6zwXCUp3/xCgAel8Gf7Qxv3LIyb9SwxpGQlubV6ID5QgFOZIZFohH+7eS/8cUDX6Qz2Mk9y+9h++btlGWWAZDuSZ+wCqVMoPEZ+N6HIRRbu6XnLPz7/3C297VA60Fne2oWLH8d1H7Q6WUrWKvCI3LFOp76MoF9+2h+fAepa9cwsHsPgYMHIRQCr5f08nLyP/C7+GpqSd9UiSs1NdlNFpEFqntgiFc6nJB2umMgFtgGON3RT08gdNnjDfD8x++iMDMNt0v/Dy4UqkIpMoN+2fRLPrf3c5zsPkllQSV/VP1HVBRUJLtZ889gn9Pj9vdvhoGOifdZfmush+2OWOER/X1Krly4s5NAfT39zz/PhX/+hjNnEsAY0so3klHjlPX3bd6Ey+dLbmNFZEEZDmlnOgdGwtoEIc0YKMlOZ0V+S8Jq0QAAIABJREFUBsvyfKzIz2B5XgbL8328+2/3cL4nOO7cpTnp/Opjd83m7cg0URVKkVl2vOs4n9/3eZ4//zxLMpfw+Ts+z+uXvl5jxycSCUHveSeg9TY5vWo956CnKfb5HAz2/H/27ju+6Wr/4/jrJE2bpJsWKLSFFkGGUFqWA3ErKIrjet16Hfd65V6F67qCd4heFBDcitc9fuq914uKIOj1Kg6crJYNFyirpdBS6EzSZpzfH980TRerI235PB+PPpp88x0nJpW88znjMCdRcPMnbdJc0fFpj4eqLVtw5uTgzMnBkZ2De9cu40GlasOb2Uzs5ZfTc/rfQtdYIUSnUOKoDoQyI6xVsr3Ywc7iSkocDUNaWqKdizN6+MNaJOmJdlLi7U12c/zjuAEdemFqcXQkwAnRggodhTyf/Tzzt84nOjyaP478I9f0vwbL8ToNvdbgKK4NYqV5RkArCwpn5Xuh/kQuti4QmwzxvY012GKSjfFun02FysKG14lNaZOnIzomb0kJztWrceTk4MzOwbVmDT6HAwBzYiL2rEzir74KS1oae+65F11V5T/QS9nChXSbPCkwFk4IIZoSHNKMLo+HD2njh/TwV9EOH9IOpaMvTC2OjgQ4IVqAw+3gzfVv8ub6N3H73Nw46EZuz7id2IjYUDetdVU7mqiaBYU0T70uHWHW2kB2wrlGUItNMX5iUoz7TU3jr311x8ABWGzGRCZCANrno3rbNhzZ2ThzVuPMyaE6N9d40GzG2r8/sZddhi0rC1tWJpbk2jUWC6Y9jPb5GpyvaO6L9HhI3mNCCCh1uNnuD2c7Ar+NsWlHEtLSEuykdjm2kHY4l2UlS2A7TjQrwCmlxgHPAGbgVa31zCb2+wUwDxiptZYBbqLT8Pq8fLztY57Pfp4iZxFj08YyedhkUqM7wSyHPq9RHWusalbz4zxQ7yAF0UlGGEsaAieOg9hUf0BLNm7bE459QpGa2SabmoVSHHe85eU4V68JdId0rl6Nr7wcAHNcHLasLGIvvRRbZia2IYMPOX7NmZNjTFQSzO3GmZ3dmk9BCNHO1IS0ncVGd8cjCWkXDelBehuENCGgGZOYKKXMwP+A84E8YDlwrdZ6Q739ooFFQDhw5+ECnExiIjqKH/J/YM7KOWw5uIWhXYdy34j7yOyWGepmHRmtwVXSMJAFxqHlGePSdN0piYmIra2WBSpnqbUVtegeECZTqIvWobWmevsOI6hlZ+PMyaFq61bj/awUESeeaAS1rEzsmZlYeveWcadCiEaVOtyBKfiDJxDZWVzJwUZCWu8Eu9HNMaF2AhEJaaI1tdYkJqOArVrrXP9F/glcCmyot9/fgFnA/c24lhDtxpaDW3hixRN8v+d7kqOSmXPmHC7ofUH7+qDodgUFsaBujcETg7gr6x5jstRWyXqPDgpqNd0bk8EaE5rnI45LvspKnGvX4czJxpltVNi8pcaENqaYGGxDhxJ94TjsmZlYMzIwR0WFuMVCiPYkOKTVTMF/uJB24ZAepCXYSUuIlJAm2q3mBLhkYHfQ/Tzg5OAdlFLDgFSt9SKlVJMBTil1O3A7QK9evZrRJCFaT5GjiBdyXuCjrR8RaYnkvhH3ce2Aawk3N7PidKiFqRvj8xkTeTSonAXdrixqeFxkN+P8XU+EvufWhrKaLo6RXcFkat5zEeIYaa1x5+UFKmuO7ByqNm823u9A+AknEHX+edgzM7FlZRGeno6S96sQndr87PzDTspR6nQHjUdzBAW2uiENoGeslbTEyDohLS0xkl4S0kQH02qTmCilTMCTwM2H21dr/TLwMhhdKFurTUIcC4fbwVsb3uKNdW/g9rm5bsB13DH0jpaZoKSxhakXTDIqZUmDm5gcZA/46o3TsURCnL8rY1KGP5QFTQ4S3RMs1ua3V4gW4nO5cK1bFwhrzpwcvMXFAJgiI7ENzSD6jt8ak41kZGCO7eQTAgkh6pifnV9nWvz8Eif3z1vNZ+v2Yg83ByYSaSqkjRvcg/RECWmic2pOgMsHgmdqSPFvqxENDAa+9nctSwIWKKUmyEQmoiPw+rws2LaA57Ofp9BZyPm9z+cPw/5Ar5gWrBJ/Ma3ujIoAHid8Oa32vjL7K2XJkDqqYeUsNhmsccc+MYgQrUxrjaegoM7MkK6NG8HjASC8d2+iTj89MDNkRN++KLN80BLieFLl8bL7gINtRUY3x2e/3FJnTTMAt1fz2fq99Iy10juhNqT19nd3lJAmjhfNCXDLgX5KqXSM4HYNcF3Ng1rrUiCx5r5S6mvgPglvoiP4cc+PPLHiCTYf3ExGYgZzzppDVresljl5ZTFs+gQ2fGyMT2vKbf81glp0EpjkHyTRcfiqq3GtXx8Ia87sbDyFxvp9ymbDNmQICbfeakw4kjmUsC5dQtxiIURb0Fqzr6yK3KIKcvdXkltUSe7+Crbvr2T3AQe+I+iDpYAfpp7b6m0Voj075gCntfYope4E/oOxjMDrWuv1SqlHgBVa6wUt1Ugh2srWg1t5YuUTfJf/HclRycw+YzZj08Y2f4KSikLYuNAIbTu+M2Z3jE+DiGioKm+4f2yqUW0TogNw7yusncY/OxvX+vVo/3T8lpQU7KNGBWaHtPbvjwprv0uQHsmYGyHEoZW73Gzfb1TSthVVkltUEbjvqK6tqtksZtITIxmcHMulQ3uS3jWSPolRpCVGctEzS8kvcTY4d884W1s+FSHapWb9K6q1Xgwsrret0dVOtdZnNedaQrSm/c79vJDzAh9u+ZDIsEjuHX4v1w68lghzxLGftKygNrTt/B7QkNAXTr8bBl1qrJO29t+yMLXoULTbjWvT5sBkI86cHNx79gCgwsOxDh5M/E03Yss0pvIP69o1xC0+co2NuZn64VoACXFC1OP2+th9wJh6v6aSZvyupKi8KrCfSUFKvDHt/qj0LvRJjKRP1yjSEyNJirFiMjX+Ben9Y/vX+XsEI/DdP7Z/qz83Idq79vs1qBBtwOlx8vb6t3l93etUe6u5dsC13JFxB3HWuGM7YWkebFhghLbdPwMaug6AM/9ohLZug+qOVZOFqUU75ykuDqqu5eBctw7tcgEQlpSELSuTLr+6CVtmJtaBA1HhHXcdwMf/s6nBmBun28v0RRtI7WIjzGQizKywmE2Emfy/zYowkwmLWREWtN3cxIfSzkYqlp2b1pqiiiq2+4NZTSUtt6iSXQcceIL6PHaJDCc9MZKzTuwaqKT16Xrs49Jq3kfy/hKioWNeyLu1yELeoi34tI+F2xbybPazFDoKOa/Xefxh+B/oHdP76E92cCds9Ie2vOXGtu6DjcA2cAJ0G9CyjReilWiPh6otW/wzQxoTjrh37TIetFiwDhoYmMbflpmJJSkptA0+Bm6vj7yDTnYWGwv3Gj/GtOPbiioPf4IjpBRYTDUBr264q9nWIAAeJiCaTbWPhZnrH9N4kGwsYNY/vrFjAm33326sSlK/YglGhWTGFUPkQ3YH46j21FbSiirZvt8Yo7a9qJLyKk9gv/AwE+kJkfTpakwaUlNJO6FrJHH2jvvljRDtUWst5C1Eh/Rzwc/MWTGHTQc2MThhMI+f8TjDuw8/upMUb6sNbXuyjW09hhrVs4GXQmLflm+4EMfIXVhI/j33kvLUk3W6NHpLSnCuXh0Ia641a/A5HACYuyZiz8wk/uqrjbFrJ52EKaIZXYrbkMvtZdcBBzv2G1WCHUFhLb/EiTeoamAPN9Ori51+3aLZV1ZFRdCH1RqJUeE8cVUmHq8Pt1fj8fnweDVurw+PT9fZ7vZqPHVu+/cJHNPweLfXFzjG6W7s/HWvFdjm89FW38GaFHUCoMVs4qCjusGkE063lwc/WsvGvWXE2cKJs1uIs1mItVmItVuIs4cTZ7NgDzc3f2yxOCpenyb/oJNt/q6O2wO/KykoddXZNznORp+ukVw+LJk+iZGkd42iT2IkPeNsx011WYj2TCpw4riRW5LLkyuf5Ju8b+gZ2ZPJwyYzLn0cJnWEiwHv3wIb5huhba8xLoaew4xK26BLoUt66zVeiGYomPYwJf/6F9EXjiPylFOM2SGzs6nevt3YwWzGOmCAf1ZIo8JmSe7Zrj9gl7nc7CoODme1IW1vWd0Po7E2C2kJxlTjvev8ttM1KiLwPDtiRcnrazxINhUQgwNmnW2HCJ81x7trjvH6cPs07/28q8l2hZtNVHt9TT5uMStibeHE2sICoS7WbiHOFk6szWIEP7vFf9u/zWYhxmaRAHEIWmsOOtzGLI/1uj3uLHbUeU1irGH08Qczo6JmdHlMS4jEFi4zHwsRaoeqwEmAE51esbOYuTlz+WDLB9jCbPwm4zdcP/D6I5ugpHCjEdg2fAyFG4xtKaP8oW0CxLXgmnBCNJP2+fAUFeHOy8Odn091Xh5VW7ZS/tlnBJdqzPHxQWEtE9vgwZjs9hC2vCGtNQcqq9lR7GDXgUp27PeHtANGSDtQWV1n/67REaQl2OnVJdIIa4mR9O5ihLSj6dolY7qO3OiZSxqdJTA5zsZ3D5yNy+2jxFlNicNNicNNqf92qdNNibORbf7fjVVBg8VYw2pDXSDkWQIVvxh/2Iuz11YAY2yWTrU+mMvtZUdxZaCCti1obFqps3Zha4tZBdZI69M1MjCBSJ/ESLpEhrfrL2mEON5JgBPHJZfHxf9t+D9eW/caVZ4qftn/l0wcOpF4a3zTB2kN+9bXhrb9mwEFvU71j2m7xFg4W4gQ0FrjPXAgKKDlB2678/Jw79kTmL6/hrJaA5OOYDYTc+E4es6e3S4+uPl8mn3lrqBxaI46VbXgD/JKQc9YW6CCluavoPVOMCZJiIyQEQFtrbUqlm6vj1JncKirDYElTjdlTjcljuqgEFizb8MuncGsFlMg5MXagoJf/TAYtE+c3UJURFiL/b0czRcEPp9mT6kzaGxa7dppe0qddbrPJsVYAyHNGJNmVNOS42yEmY+wl4kQol2RACeOKz7tY1HuIp5Z9Qz7HPs4O/Vs7h5+N+mxTXRx1BoKVteGtgPbQJmg9+ja0Bbd8SZrEB2Tt7Q0UD1z5+UHwll1fh7u/D1oZ92Khzk+HktyMpaUFMJTkgO3LckpKEsYuRdfgq6qndJbRUTQ94v/ttn0/h6vj/wSZ51ujjv8t3cdcFDlqe3SZTErUuPt9Eqwk+YPZmmJRkhLibcREdZ5KiidRXuqWPp8mopqD6UOd1ClL7jCV7cCWOqofTz4fVif2aQCXThj7bXVvboh0Ah+MYFqoPFYcHhqKvD+5eKBDOwRU2dR65rKWnC7IsPNRvUsaAKRPonGbfkCQ4jORwKcOG4s37uc2ctns/HARgYlDOK+EfcxMmlkwx21hvxVtWPaSnaCMkP6GCO0DbgYorq1/RMQnZ6vspLq/HwjnNVU0vzhzJ2Xh6+87sLupqgoI5ClJBOenBIU0IywZo6KbPJaBdMepuSDDyC4KmexEHfllfR4qOXWGnS5veQddLBjv1E9MyYOcbCruJK8g846U41bLSZ6dzHGoKUl+kOaf0xaj1irVAtESLjc3kDFr6a6V1ov5NVW/2q3lbsO3d0zOiIsEOq2FlYcMiiCERZ7dbEHglnwLI9doyPaReVcCNE2ZBZK0enlluby1Iqn+Drva5Iik5gxZgYXpV9Ud4ISnw/yV8D6+cYMkqW7wRQGfc6CM+6D/uMhMiFUT0F0Er6qKiOM5dd2bawOqqR5Dx6ss7+y2bAk9yQ8OQV7VlYgrFmSkwlPScEcG3vMbXHm5NQNbwBuN87s7KM+V0WVp9Gp93cVOygoc9XpzhVtDSMtIZLBybGMz+jh7/JohLRu8iFUtENWixmrxUz3GOtRHefx+ihzeWorfDWhz1FNqdNDibPaHwLdrN9T1uR5XrlpRGDNNIt8iSGEOAwJcKJDO+A6wNycucz73zysYVYmD5vMDQNvwBrm/0fY5zUW1N7wsbHAdvkeMIfDCefA2Q9C/wvBdogxcULUo91u3Hv31pkoJLiro6eoqM7+ymLB0rMnlpQUrOef36Cro7lLl1YLNH3mf3TEXdy01pQ43LUVtDqThlSyv6LupCGJUeH0TojklD4JRkBLtAeqaXF2i4Q0cVwIM5voEhlOl8hwoOlqOBx60pfzB3VvpRYKITojCXCiQ3J5XLyz8R1eXfsqLo+LK0+8kolDJ5JgSwCvB7Z/a4S2jQuhYh+YI6Df+TDoYThxLFiPvaohOjft9eIpLGxyHJpn7z6jmlvDbMaSlIQlJYXIMWP8XR393RxTUgjr2hVlCs036vXH3OSXOJnywRq2FpbTKyGyTkVtR3Flg+5gPWOt9E6I5LyB3etMvd87IZIoGXMjxFG5f2z/RsfA3T+2fwhbJYToiORfYNGh+LSPxdsX8+yqZymoLOCs1LO4e/jd9IlKhR1L/aHtE3DshzCbP7RdaoS2iOhQN1+0A1prvPv3N5zFMT/PGJu2p6But0OlCOvWDUtyMvYRIwgPjD8zApolqTsqLPT/K9VaU+p0s6+sir1lLvaVuvjbog11PiwCuDw+nv9qG2CMt0mNt9ErIZKsXnGBClpaop2UeHunmnZdiFCrqXy3l0lfhBAdl0xiIjqMFXtXMGfFHNYXr2dgl4HclzWZUU6nMRHJpkXgPACWSCOsDbrUCG/hh+7SIjomd2Eh+ffcS8pTTzaYTVFrja+0tGE4y/NPFJKfXzutvp85IcE/5swfzIJmdQzr2RNT+JGvI9YaqjxeCv3BbG+pi31lxs/esqrA7X1lLlzuQ0+QEOyb+8+iZ5xNxtsIIYQQ7ZBMYiI6tB2lO3hq5VMs2b2E7vbuPHbCNYwv3I3pnWvBVQrh0cZYtkGXQt9zwWILdZNFKyt67jmcK1ey58E/ETXm9NpwlpdnzORYWVlnf1NsLJbknkT06UPUGWf4A5q/q2NycsgWsfb5NAcc1UGhrLZ6tq+8NqwddLgbHBsRZiIp1kr3GCtDU+LoHhNB9xhrYFtSjJWrX/qRPaWuBscmx9nonSBfbgghhBAdkQQ40W4ddB3kxdUv8u/N7xOuzEwKT+XGrWuxrl8OEbEw4CIYdJkxi6Tl6GYOEx2Lt7wcx8qVOJYtp/KHH6jatAmAyqVLqVy6FGW3B8ad2UeONMJZSm0lzRzd9t1nHdUe9pa62FvmqlM9KwwEsyoKy124vXV7QSgFiVERJMVYSYm3Mbx3PEkxVroHBbOkGCsxtsMvLvzHcQNkzI0QQgjRyUiAE+1OlbeKd9e+wSvrXsXpreIXFS4mFu8nMaIYBk0wQlv6mRAW2m5tovV4S0sDgc2xbBmuTZvA50NZLJjiYsFkMiYSCQsjdsIEejw6vc1mPfR4feyvqDYqZTVdGf2BzOjWaGxrbH2oqIiwQKXs5PQuRiiLjqitmsVaSYyKaLFujTLmRgghhOh8ZAycaDe0q5xPlz3BMzsWskdXc6bDyd0OzQknXmx0j0wbA2ZLqJspWoHn4EEcK1bgWL4cx7LlVG3eDFqjwsOxZWZiHzkS+6hRWHr2IPfiS9BVVYFjVUQEfb/4b4OxcEdLa02Zy1MvlAV1a/Rv219Rha/e/zbDTIpu0RF0q6mQxVrpFhMRqJbVVM9k5kYhhBBCHAkZAyfaL1cp/O8/rFr3HnMqNrE2wsIAt4+HE0Zyyhm3Qq/TwCxv087GU1yMY7k/sC1fTtX//geAslqxZWWSeNedRI4ciTUjA1NEROC4gmkP4/V6Ca5Peb1eiua+SI+H/trk9ao9PvaV1XRfrPJ3a3QFdWusYm+pq8GMjQBxdgvdo40QNiApmu4xtV0Zu8dY6R4bQWJkBCaTrHsmhBBCiNYnn4xF23MehM2fwoaP2bnzG56OjeSLSDvdbFFM73cNl4ycjEkqbZ2Kp6gIx/LlVPoDW/VWYxp7ZbNhz8oi5qKLsI8aiW3wYNQhZnzc++NyrJ66XRNNHg+7l/7Epk2FDbo17i2rorDMRXFldYNzhYeZ6O6vkg3qGcM5A7rVVsuCujXKVPpCCCGEaE+kC6VoeWvehy8fgdI8iE2Bc/8KJ5wLmxcZ67Tlfk0JPl7qlsw/bSYs5nBuG/xrbhr8K2xhMoNkZ+DeV+jvDrnMCGzbtwNgstuxDR+OfdRIo8J20kkoixHWPV4fZS4PJY5qSp1uSpxuSh1uShzVlDjdlDjc/Gv57karZPUlRoUHKmWBiT9ig7o4xliJs1vabNycEEIIIcTROFQXSglwomWteR8WTgK3s3abMoHWgKY6rhfv9TqJl525VHqruKLfFfw+8/ck2hJD1mTRfO6CgkB3yIqfl+HZtQsAbY/ENXAIJf0Gs7fPIPYk9uJgla82nDn9Yc3hbnTSj2DREWGUVzW9zwcTTyMp1krXqAjCw2RtMyGEEEJ0XK02Bk4pNQ54BjADr2qtZ9Z7/A7g94AXqABu11pvaM41RTv35SN1wxuA9qEjovnP2D/z9PaPyC9fz+nJp3Pv8HvpG983NO0Uh6S1przK46+AGUGrxOGm1Gn8VOflYd+4hi5b19Nzxwa6lO0HoMJiY21CH9YOvoS1iSeQG9sTnzKBC9jgw2zaSZzNQqzdQqzNQteoCPp1iybWZiHObgk8FmcL9/+2EGcPJ8YaRpjZxOiZS8gvcTZob3KcMd2+EEIIIURnd8wBTillBl4AzgfygOVKqQX1Atp7Wuu/+/efADwJjGtGe0V7V5rHokg7z8THsTfMTJLHy6UVFfxgs7Fm7QucGH8iL53/Eqf1PC3ULW035mfnt9o0726vL1DhKg2qdhmhzE1pUHfFmoBW4qimzOXBWzPVotYkOQ6QsX8bQ/ZvY8j+XLo7DwJQGRHJrtT+rDl5HGUnDsGX3oeYSCsj7BbOs4UT5w9qNQEtKuLwa5cdyv1j+8u6ZkIIIYQ4rjWnAjcK2Kq1zgVQSv0TuBQIBDitdVnQ/pFA++qvKVqOzwtfz2BRpI1piV1wmYwubAWWMP4eH0e0T/PIaY8w4YQJmE0yKUSN+dn5dQJJfomTqR+uBWrX8NJa43R764SvUn9FrMTprhPQah83fioO0eVQKYix1oarWJuF1C524qxh9KjYT+qujSRuW0/U5rWEFRcZx8TFYTt9JNEnn4x95Egi+vVlhKntuivKumZCCCGEON4d8xg4pdSVwDit9a/9928ETtZa31lvv98D9wDhwDla6y2NnOt24HaAXr16Dd+5c+cxtUmEiOMAfPBr2PYlF6SlU6AaTjKRZInhv9d9H4LGtS2fT1Pl8eFye3F5vLjc/ttub+12t48qj7HtscWbKHW6G5wn3Gyid4I9MFas2utr8poWsyLWX+2KC4Sx8NpuifaakObf5t8ebbVgNim01lRv3x5YNNuxfDmeIiOwmRMTsY8cgX3kSCJHjSL8hBNk4g8hhBBCiFYW0nXgtNYvAC8opa4D/gz8qpF9XgZeBmMSk9Zuk2hBe3Lg/RuhfC9c8gwF655odLd97vI2bphRuary+Khy+/xhKjg81QYrlz9YVbmDtnu8Qcc1DF4N9vOfp9rTdNA6GtVeH/26R/m7H9YNZzE2Y4xYTTizWcxHFaq01lRv3Uqpf9Fsx4oVePcbY9jCunbFPmqUf+HskYSnp0tgE0IIIYRoR5oT4PKB1KD7Kf5tTfkn8GIzrifam+x3YdE9YE/Ae/Ni3ihZ3eSuMZauVHtqg1RN6AmuSh2qalW7X/C+9YJXndBlHHOsk6yaTQprmIkIixlrmAmrxWzctpiwhplJjArDajH7f0xEhJmJ8D9Ws80atL9xvH9bWO3tX7z4A3tLXQ2unxxnY+71w4+t8fVon4+qLVuMsOafKdJ70BjDFpaUROSppwam9bf07i2BTQghhBCiHWtOgFsO9FNKpWMEt2uA64J3UEr1C+oyOR5o0H1SdECeKvhsCqx4HdLPoOCimUxdOZuV+1aiqnvjC9uDMtV2C9Q+C3t3nM2Jf/70mC9pDQo/NbdrwlWcPZyIMFO94GSuDWD1g1SDfY0AFrzNYm6bcV1Txg1o8Uk5tNdL1ebNgYWznctX4C0tBcDSsydRZ5yBfdRI7KNGYUlJkcAmhBBCCNGBHHOA01p7lFJ3Av/BWEbgda31eqXUI8AKrfUC4E6l1HmAGzhII90nRQdTmg/v3wT5K2D0ZD7rczKP/Pc3eLWXv502nT+8ZiYsJoeIrv9BWUrQ7jiqisbiKcvivgtO9AeloGAVZqpTyQoOaTVVrYgwU6cNGS0xKYf2enFt3BQYv+ZYuRJfmTF/kCU1lahzzzW6RI4cSXiKTPYhhBBCCNGRyULe4sht/xb+fQt4XFRc8iQzStewYNsCMrpmcF/mw8z6pIifcg80emhynI3vp5zTxg3unLTHg2vDBqPCtmwZzpWr8FVUABDeu7dRXfMHNkuPHiFurRBCCCGEOFohncREdAJaww/PwRfTIOEEci74K1PWvkBBZQETh07kROvl3PbaOpzVXq4dlcr87D2yTtdRchcWkn/PvaQ89SRhXbvWeUy73TjXrcOxfAWO5ctxrlyJz+EAIDw9nZjx42sDW/duoWi+EEIIIYRoIxLgxKFVlcPHd8KG+XgGXMIr/Ubw0k9/ISkyiVfOe50vcuz8+ttVDEiK5vnrhtG3WxQnpyfIOl1Haf/cF3GuXEnR3BfpPnUKrrVrje6Qy5bjyM5GO50AhPc9gZhLJxA5ahT2ESMahD0hhBBCCNG5SRdK0bT9W+Cf10PxFnafeS9THZtYXbSaS/pcwk0n/oEH5m1h9e4SbjilF38ePwirRRboPhzt86GdTnxBP+49e8i7axK43cbq2uHhUFUFQMSJJwaqa/aRIwhLSAjxMxBCCCGEEK1NulCKo7dhAcz/HTosgk/GPsijuR9gwsSsMbOgMotfvpgNwNzrh3HRkM4zzkprjXa70Q4HPpcLn8OJz+moDV117rtqbzuCQ5kDXXPf5ay97XSiXQ2XDKjXAMLTetP1zjuNClt8fNub+0fbAAAgAElEQVQ8cSGEEEII0SFIgBN1eT2w5G/w/dOUJWcxPX0wn25+m2HdhjHt1Om8+lUJ7/68iszUOJ67NovULvZGT3OoMV3N1aCK5XCinY66913BoaphyDL2dzUMXE4neL2Hb0QQFR6OyWZD2e2YrFb/bRvm+Hgstp6B+yab/3G7DWUz7mt3NXunP2pU32r+2+3YiT0zU8KbEEIIIYRoQAKcqFW5H+bdCtu/YUXGZTzo3UPhnu+4K+suzux+NRPfXM2mveX89ow+3De2/yHXStv//As4V65k35wnSPzt7XVDlsNfmXIeImTVq1wFhyzt7154xEwmI0TZbJiCfpTdhiU+3rhvt6GstsDt2v3t9e4H3fYHNhV27H9GBdMebrBN+3wUzX2RHg/99ZjPK4QQQgghOicJcMKQvxL+dRPuyiJePPlqXi38iZToFN6+8G0274znsud/xBZu5o1bRnJ2/6ZnOvRVVlL49NOUvP8+AGUff0zZxx8f9vJ1qlhBQcncJR6LLdkISjVVrJrAZQu6b7M2HrjsdlR4eLtdR86Zk1On+gaA240zOzs0DRJCCCGEEO2aBDgBK9+ExfezMyaJKYNPZV3hj1ze93LuHHofMxbl8lH2Gk7tk8DT12TSPcba6Cm01pQtXEjhnCfwFBYak3FoDWYz9lGj6HLD9XUrV4FKlt0IX+bjcwKUPvM/CnUThBBCCCFEByIB7njmdsHi+9DZ/8dHfUYw01SOxbWfJ896kh5ho7j676vYWVzJPeefyO/P7ovZ1HgVy7l2LfsefQxnTg4RAwbgPXgQXVNV8npxrlqF7fFZMuW9EEIIIYQQzdT0ICbRuZXsgjfGUbL6Xe4ZdCoP6UIyumYw75J55Of15Yq5P+Co9vCP35zCpHP7NRrePEVF7Jn6IDt+eRXVeXn0eOwxbJlDqb8wRc2YLiGEEEIIIUTzSAXueLRtCcy7jZ/C4E99B3Ggah/3Dr+XS9Ov5YEP1vL5hn2cM6Abc345lC6R4Q0O91VXc/Dtt9k/90V8bjcJv76NhDvuwBwVxYG335YxXUIIIYQQQrQSCXDHE63huyepXjKd53qm82a4m3R7As+PeRVHRXcufu57Cstd/Hn8QG47Pb3BxB9aayq++pp9s2bi3rmLqHPOofsf7yc8LS2wj4zpEkIIIYQQovVIgDteuMpg/kRyt33GA31OZJPPwdX9r+buYffw1g97eOLzn0iOszHvjtMYmhrX4PCqrVvZN2Mmld9/T/gJJ5D6yitEjTk9BE9ECCGEEEKI45cEuONB4Ub0v67nfXcRs1NTsVsieG70LE6KO5U7/i+HpVv2c3FGDx67YggxVkudQ72lpRS98AIH330Pk91O9wenEn/ttSiLpYmLCSGEEEIIIVqLBLjObt2HFC+8i4cSYvkmJo7RPU5h+unT2ZQHFz6zlHKXmxlXDOGakal1ukxqr5eSf8+j6Jln8JaWEnfVL+k6aRJhXbqE8MkIIYQQQghxfJMA11l5PfDFQ3yX8yp/7pFEudnMlBH38st+V/PMl1uZ+/U2+naN4t1fn0z/pOg6h1YuW8a+x2ZQtWkT9hEj6P6nB7EOHBiiJyKEEEIIIYSoIQGuM6oopOrfN/NU+QbeTepG39h0Xj7zcSJVKte9sowVOw9y9YhUpk04CVt47QLa7vx89s2eQ/lnnxHWswfJTz9F9NixDSYzEUIIIYQQQoSGBLjOZvdy/vfBjTwQqdgaG831A6/nD8P+wLebS7h/3lI8Xh/PXJPJpZnJgUN8DgfFr75K8Wuvg1Ik3nUnCbfdhslqDeETEUIIIYQQQtQnAa6z0Brfsld474fpPBUfS3RELHPHzGBU0mnMWLyJN3/YweDkGJ6/dhhpiZH+QzRlixZTOGcOnr17iRk/nm733YulR48QPxkhhBBCCCFEYyTAdQZuJ0ULfs9fCr/l+y6xnNnjNB4e8xhllVZ+8eIPrMsv45bRaUy5cAARYUaXSee69ex77DGcq1YRMWggyU/MwT58eIifiBBCCCGEEOJQmhXglFLjgGcAM/Cq1npmvcfvAX4NeIAi4Fat9c7mXFPUc3AHX/37ah4KK8cRGcWfR07hqgFXs2D1Hh78cDmWMBOv3DSC8wd1B8BTXEzhU09R+sGHmOPjSfrbI8RdcQXKbD7MhYQQQgghhBChdswBTillBl4AzgfygOVKqQVa6w1Bu2UDI7TWDqXUROBx4OrmNFjUcm5axJwl9/B+ZDgD7KnMOv9Fkuy9eOCDNby/Io+RafE8c00WPeNs6OpqDrzzLvvnzsXnctHl5ptJ/N1EzNHRh7+QEEIIIYQQol1oTgVuFLBVa50LoJT6J3ApEAhwWuuvgvb/CbihGdcTNXw+Nn4xlQd2fsz2yHBuPuEy7jr1L+QWuZjw+vdsK6rgrnP6MvncfoSZTVR88w37ZsykescOIs88g+4PTCGiT3qon4UQQgghhBDiKDUnwCUDu4Pu5wEnH2L/24BPG3tAKXU7cDtAr169mtGkzs/nOMDbH/ySZ7z76BJh5+WznuSU1DN5b9kuHlm4gRibhXduO5nRfROpys1lz8yZVH67lPC0NFJf+jtRZ54Z6qcghBBCCCGEOEZtMomJUuoGYATQaHrQWr8MvAwwYsQI3RZt6oj27fiWP33xe362wLkxfZl24RuYVBR3vpfNorUFjOmXyJNXZdKFavbNnMWBd97BZLXS7YEH6HL9dajw8FA/BSGEEEIIIUQzNCfA5QOpQfdT/NvqUEqdB/wJOFNrXdWM6x3X/vvtw0zb+j7uMMXD/W/m8pPvZXVeKXf9Yyl7Slw8MG4At4/uTdn8j9j21NN4Dx4k7spf0HXyZMISE0PdfCGEEEIIIUQLaE6AWw70U0qlYwS3a4DrgndQSmUBLwHjtNaFzbjWccvhLGHWgmv50JXHSaYIZl7wCr2ShvHq0u3M+mwT3WOsvP/bUxlYtI2dV99P1YaN2IYNo/srL2M76aRQN18IIYQQQgjRgo45wGmtPUqpO4H/YCwj8LrWer1S6hFghdZ6ATAbiAL+rZQC2KW1ntAC7T4urN35NVO++gO78fCbqH5MvPRdyqvDuO2t5Xy1uYixJ3VnxujuOJ+Zzs7FiwlLSqLnE3OIuegi/P+9hRBCCCGEEJ1Is8bAaa0XA4vrbftr0O3zmnP+45XX5+W17x5ibu58unp9vD7wNkacdi8/5RYz+Z/ZHKx0M33cCYxd9yWFV7wKWpP4u9+R8OvbMNntoW6+EEIIIYQQopW0ySQm4sjtKc9n6qe3sMpZwDi3ib9c+AaRPUfw9Bf/49kvt5DWxc7rJ5US/sjv2L+ngOhx4+h+/31YkpND3XQhhBBCCCFEK5MA144s/t9HTP/xYXw+N49ZenHxDf+g0G3l9ld/4qfcA/w6yc11y1+j6rWVmAcMoOfMmUSOGhXqZgshhBBCCCHaiAS4dqC8upzHvp3KJ/nfMNRVxYy+15J69kN8tWU/976/nPDyUt6r+In4jxfjiY0lado04n55JcpsDnXThRBCCCGEEG1IAlyIZRdmM3XJZApcB/hdRTW/Gfs8vvTzePTTTbz+zVZu27+Cy9d8Ck4n8TfeQNff/x5zbGyomy2EEEIIIYQIAQlwIeLxeXhp9Yu8vOYVerjdvOWLJ/P6f7DL15W7XvoR84qfeXfrYmKL8okcPZruU6cQ0bdvqJsthBBCCCGECCEJcCGwu3w3U76+jzUHNjChvIKpyRcQdfHTLNpYwtNvfsivcuYzfM96LL170X3uXKLOPkuWBRBCCCGEEEJIgGtLWmsW5i7k0R//htnt4vEDJVx45iO4Mm7koXkrMb/7Jk/mLiXMGkG3++4l/qabMIWHh7rZQgghhBBCiHZCAlwbKa0qZfpP0/lsx2cMd1Uzw2mmx9Xz2RLWl/+bNJuxP35El6pyoi+/nKR77iasa9dQN1kIIYQQQgjRzkiAawPL9y7nwaVT2e8oZPKBEm6Jz8B0/Rt8smAV+rkpXHswj+r+g0ibPg3bkCGhbq4QQgghhBCinZIA14rcXjdzV8/ltbWv0Uub+L89BQweOZGSfrfx7S1T6bf2B8oi44h8eDoDrrpCxrkJIYQQQgghDkkCXCvZUbqDKUunsL54Pb9wevjjwTKsF77I+v/m4b57Ar19PnLHXcUFf7sfS3RUqJsrhBBCCCGE6AAkwLUwrTUfbvmQWctnEu7TPFVYzLnWnpSd8Ec23/U81v37WNtrKP0fepDxozNC3VwhhBBCCCFEByIBrgWVuEqY9uM0vtz1JSebonl0xyZiY84m9ycr1SsfpyAmiWXXTGHS/dfSJVJmlxSty+12k5eXh8vlCnVThBBCHCesVispKSlYLJZQN0WITksCXAv5cc+P/Pm7P3PAdYD7XGFct3MTxQfPIfcfG6m0WPm/zCvIuONmHjqzr4x1E20iLy+P6Oho0tLS5D0nhBCi1WmtKS4uJi8vj/T09FA3R4hOSwJcM1V7q3l21bO8teEt0q1deb7gAN03Wshdl47HsZFFaaey5NTLePyW08lIiQt1c8VxxOVySXgTQgjRZpRSJCQkUFRUFOqmCNGpSYBrhm0l23jg2wfYfHAzV0f24Xdf/sjBNd3ZV+xhe680Zo26kCGnD+P9K4YQY5WuBKLtSXgTQgjRluTfHSFanwS4Y6C15l+b/8WcFXOwm63MPZhA2tur2ZefgLd7Is+deTHfdB3ItAmDuXpkqvzPTAghhBBCCNEiJMAdxqLcRTyz6hn2Vu4lKTKJWwffytL8pXyb9y1nRQ7k3nlrca3RVFpi2Dj+aqaYT6J3UjwLrh/Gid2jQ918IYQQQnQAO3bs4OKLL2bdunUtfu6vv/6aOXPm8Mknn7BgwQI2bNjAlClTjulcaWlpREdHYzabCQsLY8WKFS3cWiHE4ZhC3YD2bFHuIqb9MI2CygI0moLKAh79+VF+2P0dj+8ewF2P5uDMAcsZo5l5wwzusWRyxcl9WHDn6RLeRIczPzuf0TOXkD5lEaNnLmF+dn6rXu+iiy6ipKSEkpIS5s6dG9j+9ddfc/HFF7fqtY/FzTffTHp6OpmZmWRmZpKTkxOahqx5H54aDNPijN9r3m/1S3a01+r555+nb19jwqj9+/cHtmutmTRpEn379iUjI4NVq1aFpH2LchdxwbwLyHgrgwvmXcCi3EWter2O9vo19bfWXl6/Gu7CQnbccCOeDjbea8KECccc3mp89dVX5OTkSHgTIkQkwB3CM6ueweV1EVehmfaOh9gKTd98zWNve0h7Zx2W+Ej2PzyDK3pczYqKMJ65JpOZv8jAFm4OddOFOCrzs/OZ+uFa8kucaCC/xMnUD9e2aohbvHgxcXFxDT5UtiWPx3NU+8+ePZucnBxycnLIzMxspVYdwpr3YeEkKN0NaOP3wkmtHuI62ms1evRovvjiC3r37l1n+6effsqWLVvYsmULL7/8MhMnTmzpZh5WY18MTvthWquGuI72+kHjf2vt4fULtn/uizhXrqRo7ostdk6Px8P111/PwIEDufLKK3E4HDzyyCOMHDmSwYMHc/vtt6O1BuDZZ59l0KBBZGRkcM011wBQWVnJrbfeyqhRo8jKyuLjjz9ucI0333yTO++8EzDC8qRJkzjttNPo06cP8+bNC+w3e/ZsRo4cSUZGBg899FCLPUchRPNJF8pD2Fu5F4BffOdjwG6Y9q6X5ANwMBK633Ayz530B974OZ8hyXaeuzaLtMTIELdYiMY9vHA9G/aUNfl49q4Sqr2+Otucbi9/nLeGfyzb1egxg3rG8NAlJzV5ztmzZxMREcGkSZO4++67Wb16NUuWLGHJkiW89tprfP/996xYsYIpU6awbds2MjMzOf/88xk/fjwVFRVceeWVrFu3juHDh/POO+80OZY0LS2NX/3qVyxcuBC3282///1vBgwYwIEDB7j11lvJzc3Fbrfz8ssvk5GRwbRp09i2bRu5ubn06tWL/v37s337dnJzc9m1axdPPfUUP/30E59++inJycksXLiw7dYz+nQK7F3b9ON5y8FbVXeb2wkf3wkr32r8mKQhcOHMQ162s71WWVlZjV7/448/5qabbkIpxSmnnEJJSQkFBQX06NHjkP99jsasZbPYdGBTk4+vKVpDta+6zjaX18Vfv/8r8/43r9FjBnQZwAOjHmjynJ3t9WtKW7x+AHsfe4yqjU2/hgC6uhrnmjWgNSX//CdVGzeiDtH2iIEDSHrwwcNee/Pmzbz22muMHj2aW2+9lblz53LnnXfy17/+FYAbb7yRTz75hEsuuYSZM2eyfft2IiIiKCkpAeDRRx/lnHPO4fXXX6ekpIRRo0Zx3nnnHfKaBQUFfPfdd2zatIkJEyZw5ZVX8vnnn7NlyxaWLVuG1poJEybw7bffcsYZZ6CU4oILLkApxW9/+1tuv/32wz4vIUTLalYFTik1Tim1WSm1VSnVoB6vlDpDKbVKKeVRSl3ZnGuFQpIlhrgKzTlrNCag5wH4dBg8ekckN8bcxhs/53Pr6HTmTTxVwpvo0OqHt8NtPxJjxoxh6dKlAKxYsYKKigrcbjdLly7ljDPOCOw3c+ZMTjjhBHJycpg9ezYA2dnZPP3002zYsIHc3Fy+//77Q14rMTGRVatWMXHiRObMmQPAQw89RFZWFmvWrOGxxx7jpptuCuy/YcMGvvjiC/7xj38AsG3bNpYsWcKCBQu44YYbOPvss1m7di02m41Fi2orI3/605/IyMjg7rvvpqqqXpBqC/XD2+G2H6HO+Fo1Jj8/n9TU1MD9lJQU8vNbt6twffXD2+G2H4nO+Po19rfWHl6/GtV79tS930LtSE1NZfTo0QDccMMNfPfdd3z11VecfPLJDBkyhCVLlrB+/XoAMjIyuP7663nnnXcICzO+j//888+ZOXMmmZmZnHXWWbhcLnbtavxLuBqXXXYZJpOJQYMGsW/fvsB5Pv/8c7Kyshg2bBibNm1iy5YtAHz33XesWrWKTz/9lBdeeIFvv/22RZ67EOLIHXMFTillBl4AzgfygOVKqQVa6w1Bu+0Cbgbua04jQ2XywRK2L/WC0VsBjwksGq45WMYTLiev3jSC8wZ1D20jhTgCh6qUAYyeuYT8EmeD7clxNv7121OP6ZrDhw9n5cqVlJWVERERwbBhw1ixYgVLly7l2WefZcaMGU0eO2rUKFJSUgDIzMxkx44dnH766U3uf8UVVwSu+eGHHwLGh4wPPvgAgHPOOYfi4mLKyowq5IQJE7DZbIHjL7zwQiwWC0OGDMHr9TJu3DgAhgwZwo4dOwCYMWMGSUlJVFdXc/vttzNr1qzAt+It5jCVMp4a7O8+WU9sKtxy7F3wOttrFUqHqpQBXDDvAgoqCxps7xHZgzfGvXFM1+xsr1+b/K0dwuEqZe7CQradfwH4uzKiNb6yMpKffIKwrl2bde361U+lFL/73e9YsWIFqampTJs2DZfLBcCiRYv49ttvWbhwIY8++ihr165Fa80HH3xA//7965ynJpg1JiIiInC7pnum1pqpU6fy29/+tsH+ycnJAHTr1o3LL7+cZcuW1fmiQAjR+ppTgRsFbNVa52qtq4F/ApcG76C13qG1XgMc+9f4IXTBrj2cvVZj8bfe4oNz1miuLt7P4kljJLyJTuP+sf2xWeqO3bRZzNw/tn8TRxyexWIhPT2dN998k9NOO40xY8bw1VdfsXXrVgYOHHjIY4M/UJjN5sOOn6nZ/0j2BYiMrFsxrzneZDJhsVgCH6JMJlPgfD169EApRUREBLfccgvLli077HVa3Ll/BYut7jaLzdjeDJ3ttWpKcnIyu3fXBuC8vLzAh9G2MnnYZKxma51tVrOVycMmH/M5O9vr19TfWnt4/cAY+6Z9dT/WaJ+vRcbC7dq1ix9//BGA9957LxCmExMTqaioCIxR8/l87N69m7PPPptZs2ZRWlpKRUUFY8eO5bnnngsEsezs7GNqx9ixY3n99depqKgAjOpnYWEhlZWVlJeXA8Z4u88//5zBgwc36zkLIY5ecwJcMhD8VXCef9tRU0rdrpRaoZRaUdSOZnPatT4Rk667zaRh9/pEesbZGj9IiA7osqxkZlwxhOQ4Gwqj8jbjiiFcltW8D0djxoxhzpw5nHHGGYwZM4a///3vZGVl1fmWOTo6OvCBoCWNGTOGd999FzBm20tMTCQmJuaYz1dQYFRNtNbMnz8/NB9aMq6CS541Km4o4/clzxrbm6kzvVZNmTBhAm+//TZaa3766SdiY2NbfPzU4YzvM55pp02jR2QPFIoekT2Ydto0xvcZ36zzdqbXr6m/tfbw+gE4c3LA7a670e3GeYxhKVj//v154YUXGDhwIAcPHmTixIn85je/YfDgwYwdO5aRI0cC4PV6ueGGGxgyZAhZWVlMmjSJuLg4/vKXv+B2u8nIyOCkk07iL3/5yzG144ILLuC6667j1FNPZciQIVx55ZWUl5ezb98+Tj/9dIYOHcqoUaMYP358oIoqhGg77WISE631y8DLACNGjNCH2b3N7CmKJ9FXb+IHn2JPUTwnhKZJQrSay7KSmx3Y6hszZgyPPvoop556KpGRkVitVsaMGVNnn4SEBEaPHs3gwYO58MILGT++eR9ka0ybNo1bb72VjIwM7HY7b73VxCQfR+j666+nqKgIrTWZmZn8/e9/b5F2HrWMq1oksNXXmV6rZ599lscff5y9e/eSkZHBRRddxKuvvspFF13E4sWL6du3L3a7nTfeOLYui801vs/4Zge2+jrT69fU31p7ef36zP+oVc6blpbGpk0NJ0+ZPn0606dPb7D9u+++a7DNZrPx0ksvNdh+1llncdZZZwHGzJM333wzYMxIGaym4gYwefJkJk9uWBlevXr1oZ6GEKINqJoy+1EfqNSpwDSt9Vj//akAWusGne2VUm8Cn2itG59iK8iIESN0e1lXZPTMJQwv+y9/DHufnqqYPTqBxz1XsTLmfL6fck6omyfEIW3cuPGw3aeEEEKIlib//gjRfEqplVrrEY091pwK3HKgn1IqHcgHrgGua8b52p37x/Zn6ofVLKiuHdBts5iZ0YxxQUIIIYQQQghxrI45wGmtPUqpO4H/AGbgda31eqXUI8AKrfUCpdRI4CMgHrhEKfWw1vrQ0+G1IzXdyWb/ZzN7Spz0jLNx/9j+Ld7NTAhxeJdffjnbt2+vs23WrFmMHTs2RC0STZHXqmOT108IIdq3Y+5C2VraUxdKITqyjRs3MmDAgCYX5RVCCCFamtaaTZs2SRdKIZrpUF0om7WQtxCi/bJarRQXF9PevqQRQgjROWmtKS4uxmq1Hn5nIcQxaxezUAohWl5KSgp5eXm0p6U5hBBCdG5WqzWwOLwQonVIgBOik6pZ3FcIIYQQQnQe0oVSCCGEEEIIIToICXBCCCGEEEII0UFIgBNCCCGEEEKIDqLdLSOglCoCdoa6HY1IBPaHuhGiU5P3mGhN8v4SrUneX6I1yftLtKb2+v7qrbXu2tgD7S7AtVdKqRVNrcUgREuQ95hoTfL+Eq1J3l+iNcn7S7Smjvj+ki6UQgghhBBCCNFBSIATQgghhBBCiA5CAtyReznUDRCdnrzHRGuS95doTfL+Eq1J3l+iNXW495eMgRNCCCGEEEKIDkIqcEIIIYQQQgjRQUiAE0IIIYQQQogOQgLcEVBKjVNKbVZKbVVKTQl1e0TnoZRKVUp9pZTaoJRar5SaHOo2ic5HKWVWSmUrpT4JdVtE56OUilNKzVNKbVJKbVRKnRrqNonOQyl1t//fx3VKqX8opayhbpPouJRSryulCpVS64K2dVFK/VcptcX/Oz6UbTwSEuAOQyllBl4ALgQGAdcqpQaFtlWiE/EA92qtBwGnAL+X95doBZOBjaFuhOi0ngE+01oPAIYi7zXRQpRSycAkYITWejBgBq4JbatEB/cmMK7etinAl1rrfsCX/vvtmgS4wxsFbNVa52qtq4F/ApeGuE2ik9BaF2itV/lvl2N88EkObatEZ6KUSgHGA6+Gui2i81FKxQJnAK8BaK2rtdYloW2V6GTCAJtSKgywA3tC3B7RgWmtvwUO1Nt8KfCW//ZbwGVt2qhjIAHu8JKB3UH385AP2KIVKKXSgCzg59C2RHQyTwN/BHyhbojolNKBIuANfzfdV5VSkaFulOgctNb5wBxgF1AAlGqtPw9tq0Qn1F1rXeC/vRfoHsrGHAkJcEK0A0qpKOAD4A9a67JQt0d0Dkqpi4FCrfXKULdFdFphwDDgRa11FlBJB+h+JDoG/1ikSzG+KOgJRCqlbghtq0Rnpo311dr9GmsS4A4vH0gNup/i3yZEi1BKWTDC27ta6w9D3R7RqYwGJiildmB0/z5HKfVOaJskOpk8IE9rXdNzYB5GoBOiJZwHbNdaF2mt3cCHwGkhbpPofPYppXoA+H8Xhrg9hyUB7vCWA/2UUulKqXCMwbMLQtwm0UkopRTG2JGNWusnQ90e0bloradqrVO01mkY/+9aorWWb69Fi9Fa7wV2K6X6+zedC2wIYZNE57ILOEUpZff/e3kuMkmOaHkLgF/5b/8K+DiEbTkiYaFuQHuntfYope4E/oMx+9HrWuv1IW6W6DxGAzcCa5VSOf5tD2qtF4ewTUIIcTTuAt71f8mZC9wS4vaITkJr/bNSah6wCmPW5mzg5dC2SnRkSql/AGcBiUqpPOAhYCbwvlLqNmAncFXoWnhklNHVUwghhBBCCCFEeyddKIUQQgghhBCig5AAJ4QQQgghhBAdhAQ4IYQQQgghhOggJMAJIYQQQgghRAchAU4IIYQQQgghOggJcEIIITotpZRXKZUT9DOlBc+dppRa11LnE0IIIY6ErAMnhBCiM3NqrTND3QghhBCipUgFTgghxHFHKbVDKfW4UmqtUmqZUqqvf3uaUmqJUmqNUupLpVQv//buSqmPlFKr/T+n+U9lVvXUUgYAACAASURBVEq9opRar5T6XCllC9mTEkIIcVyQACeEEKIzs9XrQnl10GOlWushwPPA0/5tzwFvaa0zgHeBZ/3bnwW+0VoPBYYB6/3b+wEvaK1PAkqAX7Ty8xFCCHGcU1rrULdBCCGEaBVKqQqtdVQj23cA52itc5VSFmCv1jpBKbUf6KG1dvu3F2itE5VSRUCK1roq6BxpwH+11v389x8ALFrr6a3/zIQQQhyvpAInhBDieKWbuH00qoJue5Gx5UIIIVqZBDghhBDHq6uDfv/ov/0DcI3/9vXAUv/tL4GJAEops1Iqtq0aKYQQQgSTbwqFEEJ0ZjalVE7Q/c+01jVLCcQrpdZgVNGu9W+7C3hDKXU/UATc4t8+GXhZKXUbRqVtIlDQ6q0XQggh6pExcEIIIY47/jFwI7TW+0PdFiGEEOJoSBdKIYQQQgghhOggpAIn/p+9O4+Pur73Pf76zpJ9spA9gRB2QgCXAoqACspiFZcqLtjWupweb9vbntuenlN7ji22p8s5Paetp72nPfeora2i4loVBVRQAZeKC0sWCIQEsq+TyTb79/7x+81kskGAhCTweT4e85jM/H7zm+/8MpB5z+e7CCGEEEIIIcYJqcAJIYQQQgghxDghAU4IIYQQQgghxgkJcEIIMUKUUlopNd38+fdKqQeHsu9pPM+dSqltp9tOYVBK7VZKXTQKz7tBKfXE2X7ekaKUqlBKXW3+/H2l1CMj8Bwn/Pd0CsdZq5R6ZjjaJIQQZ4sEOCGEGIRSaotS6kcD3H+DUqpOKTXkpVi01vdrrX88DG3KN8Ne+Lm11k9qrVed6bHPZ0qptUC71vpT8/YGpZRPKdVhXkqUUjefwvHeVkrdN0Jt1Uqp/UopS8R9/6KU+uNIPN+Z0Fr/VGt9RudBKfUVpdSuPscdln9PWutXgEKl1PwzPZYQQpwtEuCEEGJwjwNfVEqpPvd/CXhSa+0fhTadN04lIA+D+4E/97nvGa11gtY6Afg74AmlVOZZbNOJ5NCz4PhpO8vneKx6CvjqaDdCCCGGSgKcEEIM7iUgFVgWukMplQJcB/xJKbVIKfW+UsqplKpVSv1WKRU10IGUUn9USv1LxO3vmo+pUUrd02ffa5VSnyqlXEqp40qpDRGb3zWvnWZlaHHfCoVS6jKl1EdKqTbz+rKIbW8rpX5sdhdsV0ptU0qlDdLmFKXUq0qpRqVUq/nzxIjtE5RSfzBfQ6tS6qWIbTcopT4zX8MRpdQa8/5w9zrzdrj7YER18V6l1DFgu3n/s2bFs00p9a5SqjDi8bFKqf9QSlWa23eZ921WSv3vPq9nn1LqpgFeZxSwAnhnoPMAoLXeCrQD0052bpRSP8F4z/zW/B391ry/UCn1hlKqRSlVr5T6fsRTRCml/mT+ToqUUgsGa4vp34CHBgtgSqnrzeM4zd95QcS2CqXUPypjEfNOpdR087zfbb7fWpVS9yulFprnzBl6DebjpymltiulmpVSTUqpJ5VSyYO0I/L3GzofoYs/9N5WSn3PfJ+0K6WKQ78ns92/Bxabj3Ga9/f99/Q3SqnD5rl9WSmVE7FNm6+nzHwt/1epXl/KvA1ce5LzLYQQY4YEOCGEGITWuhvYBHw54u5bgVKt9V4gAPwfIA1YDFwFfO1kxzXDzN8DK4EZwNV9duk0nzMZ44Pl/1JK3Whuu9y8TjarQ+/3OfYEYDPwnxjh85fAZqVUasRu64G7gQwgymzLQCzAH4DJQB7QDfw2YvufgTig0DzWr8w2LAL+BHzXfA2XAxWDnY8BXAEUAKvN269jnKcM4BPgyYh9/x34HHAZMAH4ByCIWT0N7aSUugDIxTg3fc0AglrrqoEaowzXYpyrYvPuQc+N1vqfgJ3AN8zf0TeUUg7gTWALRvVsOvBWxNNcDzyNcb5epvd5HsgLgAv4ygDtnYlRVfo7IB14DXhF9f5y4Q6M91YyEKokX2Kei9uAXwP/hPHeLARuVUpdEXoK4Gfm6ygAJgEbTtJetNbfiKhoLgVagb+Ym49ghN4k4CGMame21roEozr6vvnYfkFRKbXCbM+tQDZQiXEuI10HLATmm/utjthWAuQrpRJP9hqEEGIskAAnhBAn9jhwi1Iqxrz9ZfM+tNYfa60/0Fr7tdYVwH9jhI+TuRX4g9b6gNa6kz4ffrXWb2ut92utg1rrfRgfxodyXDA+lJdprf9stuspoBRYG7HPH7TWhyIC6oUDHUhr3ay1fl5r3aW1bgd+EmqHUiobuAa4X2vdqrX2aa1DFax7gce01m+Yr6Faa106xPYDbNBad5rtQ2v9mNa6XWvtwThXFyilkpQxBuwe4FvmcwS01u+Z+70MzFRKzTCP+SWMLpHeAZ4vGaO61tetZsWnwzzeT7XWzpOdm0FcB9Rprf9Da+02X8+HEdt3aa1f01oHMILxBSc5Rxp4EHhQ9a/63gZsNs+/DyPkxmKE3JD/1FofD51j04/Ntm3D+BLhKa11g9a6GiOQXmS+9sPmsT1a60aMLwmG+v5EKZWOUd3+36Exh1rrZ7XWNeb75RmgDFg0xEPeifF++8T83T+AUbHLj9jn51prp9b6GLCD3u/50O9+wCqiEEKMNRLghBDiBLTWu4Am4Eal1DSMD5Ubwah0mF3n6pRSLuCnGNW4k8kBjkfcrozcqJS6RCm1w+ye14ZRgRjKcUPHruxzXyVG9SmkLuLnLiBhoAMppeKUUv9tdk90YXTfTFZKWTGqLi1a69YBHjoJo6JyusLnRillVUr93Oxe56KnkpdmXmIGei6ttRt4BmMMowWj4tR3jFtIK+AY4P5NWutkrXU8RtfJLyul/tZs14nOzUBOdk76/k5iBuseGaK1fg2oAv62z6Ze7wGtdRDjnEa+B47TX33Ez90D3E4AUEplKqWeVkpVm6/9CYb4/lRK2YHngI1a66cj7v+yMrrcOs3QPHeox6T/6+0Amhn6ez70u3cO8fmEEGJUSYATQoiT+xNG5e2LwFatdeiD7e8wqlsztNaJwPcxupedTC3GB/qQvD7bN2JUfCZprZMwxgCFjqtPcuwajG59kfKA6iG0q6/vALOAS8zXF+q+qTACwIRBxj4dxxwrNoBOjG6XIVkD7BP5GtcDN2B05UsC8iPa0AS4T/Bcj2NUZ64Cuvp2N41wGKOnZO4g2zErrK/TU8k80bnp+xrAOCdTBzv+GfgnjPdd5Dnt9R4wx3tNovd74GTvoxP5qfn4eeZr/yJDe98D/Aaj6+c/R7RvMvA/wDeAVLOb5AFO8z2vlIrH6D481Pd8AVChtXYNcX8hhBhVEuCEEOLk/oQRIP4Gs/ukyYHxYbRDKTUb+F9DPN4m4CtKqTlKqTjgh322OzCqW25zPNn6iG2NGGO8BgsDr2F0HVyvlLIppW4D5gCvDrFtfdvRjTFhyoTIdmqtazECzX8pY0IPu1IqFGIeBe5WSl2llLIopXLN8wPwGXC7uf8C4JYhtMGDUVGJwwgPoTYEgceAXyqlcsxq3WKlVLS5/X2Mc/UfDF59w+xW+SYn6AaojAlK1gBFJzs3pnp6/45eBbKVUn+nlIpWSjmUUpec5LWflNb6bYywc1fE3ZuAa83zb8cImx7gvTN9PpMDo1tpmxl6vzuUB5nVyyuAO83fXUg8RkhrNPe7G6MCF1IPTBygq2jIUxjvtwvN3/1PgQ/N0D0UV2C8l4UQYlyQACeEECdhfhB8D+OD5ssRm/4eI1y1Y1QQhrQgsNb6dYxJIrZjVH+299nla8CPlFLtwA8wPpCHHtuFMd5qt9nd7NI+x27GGG/1HYzQ8w/AdVrrpqG0rY9fY4ydagI+wJiAI9KXAB9GFbIBY9IMtNZ/xZgk5VdAG8bsjqEKyYMYFbNWjMkqNp6kDX/C6B5XjTGByAd9tv89sB/4CGgB/pXef9v+BMzD6OZ3Iv9tvp5It5kzH3aYx99tthlOfm4exhg72aqU+k9znNxKjApeHcYYr+UnadNQ/TPGBC4AaK0PYlTFfmO2by2wdpDxf6fjIeBijN/tZowJVYbiDoxQW6N6ZqL8vta6GCNkv48R1uZhnOuQ7RjBuU4p1e99rLV+E+N99TxGdXsap7bEwh0Yv38hhBgXlNZn0otCCCGEGLuUUl8Gvqq1XjqEfXdjzBz56ci3TIwFyljA/Uta61tHuy1CCDFUEuCEEEKck8zuqduB/9Ja/2m02yOEEEIMB+lCKYQQ4pyjlFqNMaaqnpN30xRCCCHGDanACSGEEEIIIcQ4IRU4IYQQQgghhBgnTrhI6GhIS0vT+fn5o90MIYQQQgghhBgVH3/8cZPWOn2gbWMuwOXn57Nnz57RboYQQgghhBBCjAqlVOVg26QLpRBCCCGEEEKMExLghBBCCCGEEGKckAAnhBBCCCGEEOOEBDghhBBCCCGEGCckwAkhhBBCCCHEOCEBTgghhBBCCCHGCQlwQgghhBBizNlcvplVz61i/uPzWfXcKjaXbx7tJgkxJoy5deCEEEIIIcT5bXP5Zja8twF3wA1AbWctG97bAMC1U68dxZYJMfqkAieEEEIIIcaUhz95OBzeQtwBNw9/8vAotUiIsUMCnBBCCCGEGDMq2iqo7awdcFttZy2t7taz3CIhxhbpQimEEEIIIUZdSXMJj+x/hDcq3zjhflc/ezXXTr2W9QXrmT1h9llqnRBjhwQ4IYQQQggxKrTW7Knfw6P7H2V3zW4S7AncM/cesuKz+I89/9GrG2WMNYb7L7ifmo4aXil/hRcPv8jnMj/HnQV3snzScmwW+Vgrzg/yThdCCCGEEGdVUAd5t+pdHtn/CHsb9zIhZgLfuvhb3DbrNhxRDgAcUQ4e/uRh6jrryIrP4lsXfys8gck3L/4mLx1+iadKn+Lbb3+brPgsbp91OzfPuJnkmOTRfGlCjDiltR7tNvSyYMECvWfPntFuhhBCCCGEGGb+oJ8tFVt4dP+jHHYeJjchl68UfoUbp99IjC3mlI8XCAZ4p+odNpZs5MO6D4m2RnPd1Ou4Y/YdzJowawRegRBnh1LqY631ggG3SYATQgghhBAjyRPw8FLZS/yh6A9Ud1QzPXk698y9hzVT1mC32IflOcpay9hYupFXj7yKO+BmYdZC7px9J1dMukK6V4pxRwKcEEIIIYQ469q97Txz8BmeKH6CZncz89Pnc9/c+7hi0hVY1MhMht7maeOFshd4qvQpajtryYnP4bbZt3HzjJtJik4akecUYrhJgBNCCCGEEGdNc3czT5Q8wTOlz9Dua+eynMu4b959LMhcgFLqrLTBH/TzzvF3eLL0ST6q+4gYa0x49sqZKTPPShuEOF0S4IQQQgghxIir7qjmjwf+yIuHX8Qb8HL15Ku5d969FKYWjmq7DrYc5KnSp3i1/FU8AQ+LshaxvmA9V068EqvFOqptE2IgEuCEEEIIIcSIOdx6mMcOPMZrR19DKcXaqWu5e+7dTEmaMtpN68XpdvLC4Rd4uvRpajtryU3I5fZZt3PTjJuke6UYUyTACSGEEEKIYbevcR+P7H+EHcd3EGuL5eYZN3NX4V1kxWeNdtNOyB/08/bxt3my5En21O8h1hbLdVOvY/3s9UxPmT7azRNCApwQQgghhBgeWmver32fR/c/yl/r/kpiVCJ3FtzJHbPvICUmZbSbd8oOthxkY+lGNpdvxhPwcEn2Jdw5+04un3i5dK8Uo+aMA5xSag3wMGAFHtFa/7zP9vuBrwMBoAP4qta6WCmVD5QAB81dP9Ba33+i55IAJ4QQQggx9gSCAd469haPHniU4uZiMmIz+HLhl1k3cx1x9rjRbt4Za3W38nzZ8zxd+jT1XfXkJuRyx+w7uHH6jdK9Upx1ZxTglFJW4BCwEqgCPgLu0FoXR+yTqLV2mT9fD3xNa73GDHCvaq3nDrWxEuCEEEIIIcYOX8DHq+Wv8tiBx6hwVZDnyOOeufewdtpaoqxRo928YecP+tl+bDtPljzJJw2fEGuLZe3UtawvWM+05Gmj3TxxnjhRgBvKqoaLgMNa63LzYE8DNwDhABcKb6Z4YGz1yxRCCCGEEKeky9fF82XP83jR49R31TN7wmx+ccUvWJm38pzuWmiz2FiVv4pV+asoaS5hY+lGXjr8EpsObeLS7Eu5s+BOluUuO6fPgRjbhlKBuwVYo7W+z7z9JeASrfU3+uz3deDbQBSwQmtdZlbgijAqeC7gn7XWOwd4jq8CXwXIy8v7XGVl5Rm+LCGEEEIIcTraPG1sLN3IxpKNOD1OPpf5Oe6bdx9LcpactTXcxpoWd0t4cfCGrgYmJkw0ulfOuJHEqMTRbp44B51pF8ohBbiI/dcDq7XWdymlooEErXWzUupzwEtAYZ+KXS/ShVIIIYQQ4uxr6GrgT0V/4tlDz9Ll7+LKiVdy77x7uTDjwtFu2pjhC/rYfmw7G0s2hrtXXj/tetbPXs/U5Kmj3TxxDjnTLpTVwKSI2xPN+wbzNPA7AK21B/CYP3+slDoCzAQkoQkhhBDj3ObyzTz8ycPUddaRFZ/Fty7+FtdOvXa0myVOUaWrkj8c+AMvH3mZoA6yZsoa7pl7DzNTZo5208Ycu8XO6vzVrM5fTXFzMRtLNvJC2Qs8c/AZLsu5jDsL7mRp7lIsyjLaTRXnsKFU4GwYXSCvwghuHwHrtdZFEfvM0FqXmT+vBX6otV6glEoHWrTWAaXUVGAnME9r3TLY80kFTgghhBj7NpdvZsN7G3AH3OH7YqwxbLhsg4S4caK0pZRH9j/CG5VvYFM2bppxE3cV3sUkx6STP1iENXc383zZ8zxT+gwN3Q1Mckxi/ez13DD9BhxRjtFunhinhmMZgc8Dv8ZYRuAxrfVPlFI/AvZorV9WSj0MXA34gFbgG1rrIqXUzcCPzPuDGMHulRM9lwQ4IYQQYuxb9dwqajtr+92fHZ/Ntlu2jUKLxFB9XP8x/7P/f9hdvZt4ezy3zbqNL835EmmxaaPdtHHNF/TxVuVbPFnyJJ81fkacLY7rp13PHQV3MDXp7HSv9DU0UP3t7zDxV7/Elp5+Vp5TjAxZyFsIIYQQw6Kus46d1Tv50fs/GnSfr13wNQrTCpmTOkdCwRihtebdqnd59MCjfNrwKRNiJvDFgi9y2+zbZBKOEVDUVMTG0o28fvR1fEEfS3KWsL5g/Yh3r6zd8BDOZ54h+fbbyf7hD0bsecTIkwAnhBBCiNPiD/rZ27iXnVU72Vm9k0OthwCwKAtBHey3v03ZCOgA2lxRKDMuk8LUQuamzaUw1Qh1yTHJZ/U1nM/8QT9bK7by6IFHKWstIzs+m68UfoWbZtxErC12tJt3zmvqbuK5Q8+x6eAmGrsbyXPksb5gPTdMu4GEqIQzPn6wsxNvZSWeo0dxFxXR8sfHIRgEu51pW7cQlZMzDK9CjAYJcEIIIYQYsubuZnbX7GZn1U521+ym3duOTdm4KPMiluUu4/KJl1PSXMJD7z804Bi45ZOWU9JSQlFTEQeaD1DcXEylq2eJoNyE3F6hriC1QMYKDTNPwMNfDv+FPxz4A1UdVUxLmsa98+5lzZQ12C320W7eeccX8PHmsTd5suRJ9jbuJc4Wx43Tb+SO2XeQn5R/wsdqvx9fdTXeigo8R4/irajAe7QCb0UF/vr6wR8YFcWE9etJvnUd0VNlhszxRgKcEEIIIQYV1EGKm4t5t+pddlbtpKi5CI0mLTaNZbnLWDZxGZdmX9ovZJ3KLJQur4uS5hIONB2gqLmI4uZiqjt6JrXOT8ynMK2QwlTjMnvCbOLscSP6us9FHd4ONh3axJ+L/0xTdxPz0+Zz77x7uXLSlTIz4hhxoOkAG0s28nrF6/iDfpbmLuXO2etZFD0Lf+WxfiHNe/w4+Hzhx1uTkoiaMoWo/HzjMmUKlsREqu6/H+3x9DyRxQJKQSBA3IIFJN+6DseqVVhiYkbhVYtTJQFOCCGEEL20edp4v+Z9dlbvZFf1LlrcLSgU89Pnh0Pb7AmzR/RDf6u7leLm4nCoK2ouoqGrATC6aE5NmmoEOjPYzZowi2hr9Ii1ZzxrcbfwRPETPH3wadq97SzOXsx98+5jYdbC83bx7bEo2NWFt7ISb0UFrYeKKD+wC3d5OelNPuIjspeKiiJqch5R+VPCIc24zseWktLvuLUbHsL5/PO9gh52O0nXXUv0tGm0PvssvspjWJKSSLrhelLWrSN6xoyRf8HitEmAE0IIIc5zWmsOtR5iZ/VOdlbtZG/jXgI6QFJ0EktylrBs4jKW5CwhJab/h8OzqbGrMRzmipqM6xa3sfqQTdmYkTKDOalzwqFuRvIM7Nbzt0tgTUcNjxc9zgtlL+AJeLh68tXcO/deCtMKR7tp5y0dCIS7PHqPHsVTURGuqPnr6nrta8vJxj55MnWpFj6wHWNPdA3OjHiWfe4m7phzJ3mJeUN6zvIbb8JTWtrv/ujZs5n60ovoYJCuv/4V56Zncb3xBvh8xF50Ecm33krimtVYYmU85FgjAU4IIYQ4D3X5uvig9gOja2T1znB1q2BCAUtzl3L5xMuZlzYPq8U6yi0dnNaa+q76niqdGepcXhcAUZYoZk2YZYQ6s1o3NWkqNottlFs+so44j/DYgcd4rfw1AK6bdh13z737rE1Xf77TWhNobcUb7u5oBrWjFfiOHUNHVMIsiYlETcknulclbQpReXn9gtO+xn1sLN3I1oqtBIIBo3tlwZ0szlk8bNVwf0sLbS/9BeemTXgrKrA4HCStXUvybbcSM2vWsDyHOHMS4IQQQojzgNaaCldFeMbIj+s/xhf0EW+P57Kcy1iWu4wluUvIiMsY7aaeEa01VR1V4TAXGlPX6esEINYWy+wJs8OzXs5Nm8vkxMnnxBiw/Y37eWT/I2w/vp1YWyw3z7iZuwrvIis+a7Sbdk4KdnfjPXZswKAWdLl6drTbicrLGzCoWVNSTrkba2NXI88eepZNBzfR7G4mPzGf9QXruX7a9cTb44fltWmt6d6zh9ZNz9K+dSva6yXmgvmkrFtH4jXXYIkfnucZq176tJpfbD1IjbObnORYvrt6FjdelDvazQqTACeEEEKco9x+N3vq94RD2/H24wBMS5rGsonLWJa7jIsyLjrnuxkGdZAKVwVFTUaYK2ouoqS5JDxLZrw9vleVrjC1kIkJE8fF+DCtNR/UfsCj+x/lw7oPSYxKZH3BetbPXj/qXV7PBToQwFdba4S0oxV4K4yw5qmowF/Te7F6W1ZWeCxaZFCz5+SgbMNf9fUGvGyr3MaTxU9yoPkACfaE8OyVQ+1eORQBp5O2l1+mddMmvIePYImPJ3HtdSSvW0ds4bnXHfelT6t54IX9dPsC4fti7VZ+9oV5YybESYATQgghziHVHdXhwPbX2r/iDriJscawKHtReAKS3ISx8SFkNPmDfsrbysOVuuLmYkpbSvEFje5tiVGJ4UA3N3UuhWmFZMZljplQF9RBth/bziP7H6GouYj02HTuKryLW2beMmxVmPOJv7XVCGihapoZ1LyVx9Beb3g/S0KCEcymGLM8RoeqaZMnY4kbvZlR9zXu48mSJ9lWsY2ADnD5xMtZX7CexdmLh+09q7Wm+9NPjbFyr7+O9niIKSw0xspdey3WhHPjfbf4Z29R2+bud39uciy7v7diFFrUnwQ4IYQQYhzzBX18Wv9peAKSI21HAJiYMJHLJ17OsonLWJC5gBibTA9+Mr6AjzJnWXg8XXFzMWWtZfi1H4AJMRP6hbq02LThefJ9m+CtH0FbFSRNhKt+APNvHbCNm49u5rEDj3G07Sh5jjzunns310+7nihr1PC0ZZzwNTRQ/e3vMPFXv8SWnn7S/YNuN97KiC6PFT2BLdDW1rOj3U7UpEnhalpkULOmpo6ZED+Qhq6GcPfKFncLU5Omsn72euwWO7/f9/shLesxFIG2NtpeeRXnpk14Dh1CxcWRdO3nSb71VmLmzh3T5yjEHwhytKmTkrp2SmpdlNS6KK1tp87VP7wBKODoz0//nA0nCXBCCCHEONPY1ciu6l3srN7JezXv0enrxGaxsSBzQbjKlp+YPy4+RI11br+bQ62Hek2SUt5WTlAHAciIy+i18Pic1Dmn3nVx3yZ45Zvg6+65zx4La/8zHOK6fF28UPYCjxc/Tl1nHbNSZnHfvPtYOXnlmJ5oZiTVbngI5zPPkHz77WT/8AcA6GAQX01tr3AWuvbV1kLEZ1tbRkavKfhDQc2emzsiXR7PJm/Ay9aKrTxR8gTFzcX9tsdYY/jB4h+wdtraM3oerTXuffto3bQJ12uvo7u7iS4oIHndLSStXYvV4Tj5Qc6Cti4fxaGQVueipLadQ/XtePzGv2ObRTE9I4GC7ETeKqnH5fb3O4ZU4E6TBDghhBDno0AwwP6m/eEqW0lLCWCEh8jFtKXr3NnR5euitKW015IGFa6K8PbchNzwBCmFqYUUpBaQGJXY/0DeTjj+V9j0ZfC4+m9PmkTb13bzVOlTbCzZSKunlYszLua+efexNHfpeRvQtc9Hd3EJx774RWNGR6uV+KVL8dfU4D12rNeC1Zb4+H5rpUXlG2PUzvWJOMAIWMs3LafZ3Tzg9hhrDLG22N4Xe2z/+wa4xNnieu0f4w5ieXM33hdexVd6CBUTQ+LnP0/yuluIvfDCs/J+DQQ1R5s6zZBmBLXSWhc1EV0iU+OjKMhOZHaWg4LsRAqyE5mekUCUzZjISMbADTMJcEIIIUbNELu4DZdWdyu7a3azs2onu2t20+Zpw6IsXJh+YXgCkpkpM8/bD/FjTbu3nZLmEoqai8LLGlR3VIe3T06cTGHKLApVLIUdTgrqSomr/hSCxjf9m+PjeDglmTqblSx/gLvb2qix2dmUlkWXv4srJl7BvfPu5aKMi0brJZ41AZcLX20tvpoafLW1+Gtq8NXUGvfV1uJvaIBgsNdjLAkJxC1caIS0iKBmS08/7/+NzH98PpqBP9N/pfAry/xeQQAAIABJREFUdPu7w5cufxfdvu5e94UuofGhJ6U1U+tg5V5YUhQgxgu1mVF8emkqBxdlY0ly9IS/oVzsfQKjLRa7xY7L7ac0XFUzukEerG/H7TPeG1aLYlp6fDikFWQnUpDlIN0RfdL3xEPb/8zzR/+HoLUVSyCFm6f8DT9c8aVTOu8jSQKcEEIIcTJD6OJ2poI6SGlLaXgCkn2N+9BoJsRMYGnuUpblLmNxzmKSopOG5fnEyHO2Haf44EsUVe3iQNthioJd1Jtd85SGqXYHhSmz4fiHbIkCryViKQPzM9jnlYN7lmxg1vTVo/EShp32+/E3NJgBzQxlNdVmUDNuBzs6ej/IbseelYU9Jwd7djbWpERantwI/p5ubio6mulvvjGksXDnm1XPraK2s7bf/dnx2Wy7ZduQj+ML+nD73f3DnS8i/PUNfe0uMt47xNR3j5Be0YbPriien8wHCxIomQjdAeN4oRlhh0xb0EE7OhgFOgoL0cTZYnFExZMcG09avIOMBAcJUXEDVw1PUHV8q/ItHnr/oV5tirHGsOGyDWc0bnA4SYATQgghBtLthOYj0HwYXvv7gbu4JWTBt4vhNMcgtXvbw4tp76reRVN3EwBzU+eGq2yFaYXnxBpl54VuJxx7Hyp2GZe6faCDYLHDxAUweQlNOfMojokzAp1ZrWtxtwx4uAxl563qJvB2wKKvwpX/CLFje2mAQEcHvpoa/Ga1zFddE66c+Wpr8Nc3QCDQ6zHWpCRsuTnYs42AZs/Oxp5r/GzLzsaWloaKCLe1Gx7C+fzzELEgNnY7ybfcEh4LJ3psLt/Mhvc2jHogcRcX0/rss7heeZVgRwdRU6eSfOs6km64AUtyEm6/m8ZOFwdqmyitb6KssZnyZidVrU682o2yeFEWL6kOxYQESIrXJMQGiYkKoPHQHegfKkOXwSqQp+JUA+9IkgAnhBDi/OX3QutRI6Q1lRnXoUtn49COYY+H7Asg92LIuci4pEwBS//QpbXmiPOIMZateief1n+KX/tx2B1clnsZl0+8nMtyLhu+mQ3FyOpqgcrdULEbKndB3QFAgzUaJi6E/CUweYnxc9TAU8xrrbngTxcM+AFTodh3y3bY8RP4+I9GeFvxz3DxXaf9pcGZ0IEA/sZGs3JW0xPUIro39lrAGsBmM6pn2dnYc4xAZlTScrDnZGPPyjrlsWjlN96Ep7S03/3Rs2cz9aUXz+QlnrM2l2/m4U8eHrZZKM9EsKuLttdep/6pZ9BF+wnabByauZBX8haxPXoimN0bk2LtFGSb49SyjC6QMzITiLGf2ntfa40n4BmwW+hA3UZ//cmvBzyOQrHvrn1n/PqHgwQ4IYQQ5zatob22f0BrKgNnpVEhCYnPgNTpkDbduE6dYVw/cZMx9q2v2Akwbx3UfAJ1+8FvfsMdnQQ5F0LORXRlFvKRXbHTWcrO6l3UdNYAMCNlBpfnGtP8X5B+ATbL+J717rzQ0WgEtlBoaygy7rfFwqSFMHmpEdpyF4B96Ms2DKmLW+0+2PKAERQz58E1P4f8pcPxqsKCnZ091bJw5awm3LXRV1/fq9sigCUpqadq1qdyZs/JMapn1vNzlkxh6PD4OVjnori23Zyq38XBunY6vQHy22q5pvJDrq76mDhvN10ZOfivuZ7c224hd0rOqIxfHK4upyNJApwQQohzg9vVP6A1Hza6Qfo6e/azx0HqtJ5wljbDvD0dYgYZXzaUMXABHzSWQs2nHD+2i3eb9rLT38JH0dF4LYrYoOZSSwLLUgpYNvlqsqYsh8SckTsfY8CprtM15rTXG4GpwgxtjWbVxx4Hky4xwlr+Msi5GGynvwbbkLu4aQ3FL8G2B6HtOMy5EVb9GJLzTvocOhjE39iE36yc+fpUznw1NQQj10IDsFqxZ2Ziy8k2K2Y54UpaKKRZExJO+3WLc0swqKlq7aY4PFW/MQvksZau8D6OGBsF2YnMiZgFcmamg+igj/atW2nd9CzdH38MdjuOq68iZd064i69tFcX2pE2VrqcnogEOCGEEONHwAetFRHhrKxnnFpHfc9+ygLJk/sENDOwJeaEu+icis1vP8jD5S9SZ4GsIHxr6k1ce+WPAWPNpT31e9hZtZNd1bvCU8rnO/JYmjybZSqeBW2NRNXug4YS0OYYoISsnm6XoUvCOAw6gxhona4xzVXT0x2yYrfx/gKISoC8S43ukPnLjOqq1T6sT725fDN/eOeX3LGxlo3rs7nnim8P/mHR1w3v/QZ2/hLQsORbBC/6Kr4Wl1k5q+k1KYivpsaonvl6zyJocTh6Vc5s2dk9XRuzs7FlZEj1TAyoy+sPz/wYWgC7tK6dDo9RoVUK8lPjjS6QZvfH2dkOcpNjT1pV8xw+jPPZZ2l76S8E2tqwT5pE8rp1JN9041n7ImgsdTkdiAQ4IYQQY4vW0F7XO6CFAltrRU/4AYhPN7s69qmopeSDLXrYmjTQN7LR1mg+P+XzOD1OPqj9gG5/N1GWKBZmLQxPQJKXOEBlxNsF9Qeg+hOo+dS4NB2C0BiopEnh7pfhyxifuKIvT1kZzudfoOXxx8OzKVqSkrBERaHsdrDbUHY7ymY3r229rk+4PcoOtkG223v2I3x/VPhx/Z6nqxFV/xmq5iNU9YcoVyXKoiEmETV5sdFFMX8JZF0A1pHv4jpY4NXBIIHm5p4wFgpmx8rxHfoMf7OLgLdP0LJYsGVmmhWznAHGoGWPmUWWxdiltVFVi5yqv6TWRWVLV3hNdEe0jdnmWLXZWYkUZDuYleUgLurM/s0EPR7at72Bc9Mmuj76CGw2HMuXk3zrrcQvueysVuXGGglwQgghRoen3ezqGNHtMRTYvBHTiNtie0Ja2oyIsWlTRzzY+IN+XF4Xt7x8C43dA09qkh2fzeUTL2dZ7jIWZi0kzj7wZBUn5Gk3xjjVRIS6lvKe7SlTesJc7sWQNR9iBlgYehR5yspwbdmKa8sWvEeOGHcqZQQ4i4WoqVOJu+hCtNeH9vvRvshrn7Egs6/v/f7wz/h8vbYxwp9RjCA5cMAcUtAMbY8MkREhNLyPeR3s6qbh3//dGGNmteJYtYpAS4tRSautNV5zBEtcnFE1y8nBnmDB7vwIe6AK+5RZ2K9/ENu8K40gKwTG4tS/2HqQGmc3OcmxfHf1rH6LUnd7Axys711VK6lz0e7uGfc4OTUuXFELTTAyMeXkVbUz5Sk/ivO552h78UUCra3Yc3JIXncLSV+4GXtmxog+91gkAU4IIcTICfigtTIinEUEto66iB2VMY4nHNCm91TTHDkDzuh4qjwBD063E6fHSZunjVZPK22eNpwe477IbU6Pk1ZPK+3e9hMeU6HY++W9I/PhpbsVaj7rCXQ1n0HbsfAzkzYjokp3MWTNG3Smw5HiKSvD9foWXFu3GqFNKeIWLCBuyWU0/9fv0F5veN/hXqdLBwK9w14o5EXc1s0V6OOfoKv3omsOoDuaIKjQNgc6dQY6eTo6eSo6Ji18PMKPHyRkDrrdDJZ+X09I7RdU/f26MQ5GxcYSM3t2/8qZWT2zOBy933fBAHz2pLHYfGcTXPwlWPGDc6pLrjg9L31azQMv7Kfb19N7Idpm4UuXTiY5zk6JObnI0ebO8Pci8VFWZpshbXaoC2SWg/jo0f1SIOj10vHWW7Ru2kTX+x+A1UrCFVeQfOs6EpYtO2+6/EqAE0IIcWa0ho6GiIAWGpdWZnR5DEbMWheXGlFBi6iopUwZ8qx9Wms6fZ39wlY4jJlBLHK70+Ok29896DFjbbEkRyf3uiRFJ5EcY/z8+72/x+lx9nvcWZ+VrLMpItCZl3ZztjRlgfQCs0pnBrvMucPalVRrjaesjPYtW/uFNsea1ThWrsSekTE663Rpbbz3IicdCZ2b+HRz/NpS4zp99rB8KXB6zdQ9IdC89tXXU3n7HcMTeN1t8M6/wYe/NyZbueIfYNHfntEkK2J80lrT0O7h2v/cSVOHd9D98ibEhScUCVXWJqXEYbGc/RkgT4W3shLnc8/jfOEFAs3N2LKzSb75ZpJv/gL27OwzO/i+TcaXIW1VkDQRrvpBz4RVY4AEOCGEOF+d6h8oT0fvWR4jw1pkpcoWAxOm9Z+KP3UaxE3odchAMIDL6+oXtvqGsL7VMn/Qz2ASoxJ7glhMRBgbIKClxKSQFJ1EtPXEIWdMz0rmqu0T6j6BrmZjm8UOmXN6qnQ5F0FGwSlNwNET2rbg2rIVb3m5EdoWLsSxZjWJK1f2CxlnZZ0urY1ZISt29Uzr39lgbEvI6lmDLX+Z8UXBKExHPlQjEnibymDr96Fsm/Hvb/XPYOaq4WmwGFMCQU1VaxeHGzp6Lo3GdWT3x74UsG/DKhwxwzshz9mmvV7ad7yN89ln6dy9G5QiYdkykm+7lYTLLz/1rsRDmXV4lEmAE0KI89G+TWx+87s8nBhHnc1Klj/At1xdXLviX431rPpNxX+4p5oBGF0eJ/UKaL4J+TgdGbTaY2nzuYYUxlwe14ALGAPYlC0cvEJha8AwFmNuj04hMSoR6wgtcDzWZyUL09qYYr5vpc5tThFvjTa6W0YuPJ42s9fC0FprPIfKaN8aEdoslnClbaDQNuKCQWgoNsOaGdpCQTUxt6fClr8UJkwd04GtrxENvIe2wdYHjH/DM1bB6p8agVaMO15/kIrmTg43dFBW3xPSyhs78Ph71rNMS4hmekY8MzIcTM9I4DfbywaswOUmx7L7eyvO5ksYcd6qKmOs3PMv4G9sxJaRQdLNXyD55luImph78gMA/Gqu8X9oX0mT4P8cGN4GnyYJcEKIc8a4+YA92vxeNv/uAjYkWHBHdCOLCQbZ0NTC5zu76FYKp9VCa2wybckTcToycMYl0xYdT6vNjlNp2nwdvQJal79r0KeMtcX2DmPRKb2DWEyfylh0CvH2+FFZxPWcpLUxKUpkoKvd2zNZjD0enTUPj5pO+5EAro8O4z1WHQ5tideswbFyJba0tLPX5mDAmK2zwgxsx94zxgUCJOX1zBA5eYkx66i8Vwbn98Jf/x+886/g64JL7je6Vg627qEYVZ0eP0caO3pX1Bo6qGzpIhDs+Ww+MSWW6RkJTE9PYHpGAjMyE5ie7iAprndFbaAxcLF2Kz/7wrx+E5mcK7TfT8c779C6aROd7+4EIH7JEpJvXYdj+XKU1WJ8KdlaCc7K3tfH3hvkqAo29O9KPxokwAkhzgkj0cVNa01ABwjqIAEd6Hc7qIPhS+TtgA4QDAYJMsjtoLkvQYLBIRxrgG0DtSd8Oxgg6HER7G4l0O0k6HESdLcR9LgIuNsJ+jp5JSGOrgHGAFm0xmqx4Yucqr8PR5SjX9g6URhLjk4mxja08W3iLAoG0E1leD7cimvbdto/Lsfb4geliUv3kjgFHJfMwjZjYU+lLnnyyAWlgB/q9vVU1yrfB49ZNUzJN8evmaFtCAtXiwF0NMD2H8MnfzbGo171A7joi72qr+Lsae30crjRrKaZ3R6PNHRQ7ezpumezKCanxoWraaHL1PT4U5qmfyizUJ5TtDYq9K2V+Mo+w/nadpzvFuFv82CNheQpXSRPaSfKEfpbp4w1QpMnQ+1nxhcdfUkF7vRIgBNCDOaqTVfR0N3Q736rspKbkHviwDPI7fFMaY0FsABWrbGgsCgLFmXFarHSGnAP/EFca+6ed0+/gBb6OSk6CZvl/Jya/Fz5AGR0jzyEa8sW2rdsxXv0qFFpW7iQxNWrcFyYh819tGdJg7oDEDTHZsVO6L/w+GALo59sjGXAZ1QBK3YaVbZjH/SMpUyd3nvSkaTxd57HtJrP4PV/hOMfQPYFsOZfYfLi0W7VOUlrTZ3L3auSVtZgBLXmzp5ujTF2C9PMStr0dLOalpHA5NR47Nbzd72zE/K0D1xBC137OnvtrmNS6XBm4iyFjoNtoDXxF84i+Qs34lh7Cyo2wdhRxsANLwlwQogQrTXFLcXsOLaD7ce3U9ZaNui+10y5BquymgHG0uvnEbttsWChZ5vVYkWhwtusyrxtsYYfo5Tq2d/nQbXXYm2vw+KqweqqweKqxtJWjaWtCqu30whoWmMFLDEpWFLysCRNxpoyGZWSDymTITnfGKvWZybCVRuXUutr63eusu1JbFu/a1h/V+eC8d4FKRzaXn/dCG0VFUZoW7SIxDWrcVx99eDdI/0eY+xZzafm4uOfGbdDX3IkZPYPdeVvD/wB6LJvgjXKqLAd+7DnA1barIhJR5aCI2skT4cAo0Jx4Hl44wfgqoa5t8DKh4ywLU5ZIKg53tJFWZ+JRI40dNDh6ZlIJDHGxoxMR7jbY+iSmxw75md9POt8buMLIGfFwAGtu6X3/lEJRgUtZfIA13kQ3bNwva+uDucLL+B87jn8NbVYJ0wg6aYbSb7lFqKnTIF9m/C9/BDVW7uZuCYW29ofjpnwBhLghBDjiC/oY0/dHnYc38GO4zuo66zDoixcnHExh1oP4fK6+j3mrE/zPlR+DziPD/CH6Zjxc2hyhhB7/CB/lMzriD9MQ7G5fDMbdj2IW/fMehej7GxY+mMZN9jHseYu1v52J23d/Wdzy0mK4b0HrhqFVp2c1hrPwYM9lba+oW3lSmypqad3cF+3UZmLXHi88SCEJqRR1p6AN5CMOWZYM0Nbwvm3EO+Y4e2E3Q8bFxQs/T+w5JtG4Bb9ePwBjjZ19qumlTd14o2YSCTDEW2MSzMD2jTzOj0hWsb2hgQDxpcHg1XRek2chfHlT9KkQf4W5huzHJ/iudWBAJ3vvYdz0ybat++AQIC4RYtIvvVWOj/8kLbnniP59ttHbvmT0yQBTggxpnX6OtldvZvtx7fzbtW7tHvbibHGcFnOZSzPW84VE68gJSaFzeWbeXDXD/FpT/ixdhXNj5c+NDqBJBgAV83gXTvaayFy9sUB/zDlGX+UUiYb41WG+Y++TPoyMK01RTUuthXVsa24ntK6Ey/mff0FOay9IIfLZ6YRbRvdsUS9QtvrW/BWVhqh7ZJFJK5eg2Pl1acf2k7G02GMYav+BLb90+D7fbcc4keoDeL0OY/Btgeh+CXj/6JVP4Y5N563k8N0ePz9JhE50thBZXMnoXlElIJJKXG9KmnTMxKYlp5AUuz4npp/WGgNnY0Rf/sqev8tbKvqvU4oyphRdrAvKx3ZI7p+o6+hgbYXX8L57LP4qqp6WnW6azKOoDMOcEqpNcDDgBV4RGv98z7b7we+DgSADuCrWutic9sDwL3mtm9qrbee6LkkwAlxfmjqbuLt42+z/dh2Pqj9AF/QR3J0MldOupLlk5azOGcxsbbe3w6/9Gk139/2OGrC6yi7E+1LRrdcw09X3TUyXdy0NhZVHuiPUmvoD1PEmk6j/IdJnJgvEOTD8hbeKK7jjeJ6atrcWBQszJ/AqsIs/t+7R6h3efo9Li7KSrTNQmuXD0eMjTWFWay9IIfLpqViO0vjVsKh7fUttG85y6FtMONgGm4xiIpd8Pr3oH6/MWnMNT83lp04RzV3eMLdHcvqO8KzP9a29UyIZbcqpqTFh8enTYsIajH283wCGHfb4BU057H+k4HEp/f+25ec1/Nz0qQxseC8Dgap+vrX6Xj7HeNvvc1G8rp1Y6oKd0YBTillBQ4BK4Eq4CPgjlBAM/dJ1Fq7zJ+vB76mtV6jlJoDPAUsAnKAN4GZWg/e50ICnBDnrqNtR9lxfAfbj21nX+M+NJrchFxW5K1gxaQVXJhx4Qknz1jy87eodrr73Z+THMN73zvNLm5u1+AVNOexfgOkiUsbPKCNkT9MokeHx8+7hxrZVlTH9tIGXG4/MXYLl89IZ+WcTK4qyGRCvPE7O9EYuGvnZ7P7cBOv7K1lW1Ed7R4/qfFRXDMvi7Xzc1iYP2HYx7ZorfGUluLasrV/aFtzjRHaJkw4+YFGyjiYBECcQDAAnzwOb/0Y3E64+C5Y8eCYqpyeyqRCWmtq29y9xqcdaeigrKGd1q6eL9rioqw9E4lEXPImxI3/iURONqnQYHzunq79A31Z6e4zrX504onHoUXFj8jLG06+hgaOrFyF9vR8aTfWqnAnCnBDmWZsEXBYa11uHuxp4AYgHOBC4c0UT0+foRuAp7XWHuCoUuqwebz3T/lVCCHGnaAOcqDpANuPbWf78e0cbTsKwJzUOXztwq+xIm8FM5JnDGmswCfHWql2urnesot/sG0iRzVRo9P4N/+tvOxcytc3fsKy6WksnZHGxJS4ngf63EaVoLVy4EHSoTWnQqIcxh+hCVNh2vL+3yJGJwzjGRIjoaHdzVslDWwrqmP34Wa8gSApcXZWF2axck4my2akExvV/xv10AfDwT4wXjkrgytnZeD2zeWdQ428sreG5z+u5okPjpGZGM11841ulhdMTDrt8S/h0Pb6Flxbt+CrPAZWK/GXLGLCPfeMfmiLFPpgeDofGMXos1hhwT1QeBO8/a/GGnJFL8CVD8DC+8A6ut0D+36hUu3s5oEX9hMMBrkwL6XX2LTQRCKd3p4vX5Lj7MzISGDN3KxwYJuR6SA7MebcnEik7xcqbceN2wCFXzDGoQ32ZWVHXe9jWaN7qma5C/oHtdiUcd/ttum/focOBnvdp4NBGv/rd2OqCjeYoVTgbgHWaK3vM29/CbhEa/2NPvt9Hfg2EAWs0FqXKaV+C3ygtX7C3OdR4HWt9XN9HvtV4KsAeXl5n6usrByWFyeEOPu8AS8f1n7IjuM7ePv42zR2N2JTNhZkLWD5pOUsn7Sc7ITsIR/vaFMnv9haymv767jRuouf2h4hTvVMy9ylo/iVvp0q+1QS3DVMUg0URLcyM6aFzEAdMe7G3ge0Rpnjzgb69jD/nPjDdD4qb+xgW3E924rq+PS4E60hb0IcK+dksmpOJp+bnDIi3R27vH7eKmnglb01vH2wEW8gyKQJsUaYm59DQbbjpGFOa42npATXlq39QptjzRpj9sixEtrEuauhFLY+AEe2GzOGrvkZTD+7k/dorenyBmju8HLz796jsaN/l+a+shJjmJGZ0K+qlhofdX5NJDJYl2ZlflkV2flNWSBx4uC9SRIyz/nu/uU33oSntLTf/dGzZzP1pRdHoUX9nWkXyiEFuIj91wOrtdZ3DTXARZIulEKMPy6vi11Vu9h+fDu7qnfR6eskzhbHktwlrMhbwbLcZSRFJ53SMZs6PPzmrTKe/PAY0Tb4zqIE1u+7ixhvywkfF8RCizWdI/5UjgXSqCIDlTKZrMmzmDl7LnNnzSLKfn6ucXYuCQY1e6uc4dB2pNHo6jovN8kIbYWZzMo8eXgaTm3dPt4orueVvTXsOtxEIKiZlh7PWnMClGnpPdXbwUPbJThCs0empJy1tgsBGGOBDm2BLQ9A61GYeQ2s/gmkTjvtQ/oCQVo6vTR1eGju8NLcaVw3dXhp7vDQ3GlcN5nb3L7gSY/57+suMMenxeOIOY8nEgkGof6AsaTHGw8Ovt+y7/Tp7j9x1Cus4uTONMAtBjZorVebtx8A0Fr/bJD9LUCr1jqp775Kqa3msQbtQikBTojxoa6zLjwJyUd1H+HXflJjUrly0pWsyFvBJdmXEG2NPvmBIgX8uBsOs33nu5QVfUxesIpFCY3k+I+j+g6S7uvLf+n1h8nrD/LZcSe7yhrZdbiJvVVtBIKauCgrl0yZwNIZ6SydnsbMzITz61vacczjD/D+kWa2FdfzZnE9De0ebBbFpVNTWTknk6vnZJKbPDamRW/u8LClqI5X9tbw4dEWtIY5WQ7umOBm8fFP4Z3t+I5JaBNjlN8DH/wO3v2F8fPir8Hl34VoB8GgxuX2DRrAmju8RkAzf27r9g34FFFWC6kJUcYlPprUhCjSEqJJjY8iNSGan71W0msR7JDc5Fh2f2/FSJ+BsautyghsR3YY111Nxv0WW5/ZHk0yqdC4daYBzoYxiclVQDXGJCbrtdZFEfvM0FqXmT+vBX6otV6glCoENtIziclbwAyZxESI8UdrzWHn4fAkJEXNxn8B+Yn5LM9bzopJK5ifPh+LGkK3C183NJVB0yFoLIXGg+imQwSbDmPVPX+A/AnZ2DILjO486TNhx8+gs6H/8YbwB8rl9vH+kWZ2lTWx+3AT5U1GxSbDEc1Sc+zckulpZCbGDP2kjBO+hgaqv/0dJv7ql2NmcPZQudw+dpQ2sK24nncONtLh8RMfZeWKWemsmpPF8lkZJMWN3W+StdbUfPQZJU+9SMx7b5Pa1khAWTgyqQDrlVfxuS/eSHbe0LsUCzESur0Bo0JmhrFQAHO31rKk4v9ySdvrtKgUfmNZzxPdl+EL9v/SSylIiYsyA5gRwtLMMBYKaWkJPbcd0bYTfnl2okmFRmTW4bHK7YKKnT2hrbnMuD8hE6Ze2XOp2CWTCp1jhmMZgc8Dv8ZYRuAxrfVPlFI/AvZorV9WSj0MXA34gFbgG6GAp5T6J+AewA/8ndb69RM9lwQ4IcaOQDDA3sa94UlIjrcb/evnp803QlveCqYmTR38AN1OM6QdhKaD0GgGNucxQnMdaWWhOyGPz7oz2OvOxJsyk5VXLGPOvAX9F64exlnvqp3d7CprZGdZE+8daabF/KZ3ZmYCS6ens3RGKpdMSSU+evx3t6zd8BDOZ54ZkwuVDqS2rZs3i+vZVlzPB+XN+AKatIRoVs7JYNWcLBZPSx3T03prrXEXF9O+ZQuuLVvxHT9uVNouvRT/suVsTyvghSOdFNe6UAoumTKBtRfkcM3c7PCMmEKcCV8gSGunt1dVLBTQWsz7Ird1eQf+Xj0+ykpqQjSXRFdwf9d/M81TQm38HPbM+R7B3AVGxcwMZylx9mEfZ3oqs1CeMwI+qNoD5WaFrWqPMX7NHgeTlxiTa029EjLm9B+vfbqzUIoxSRbyFkIMmdvv5v2a99lxfAfvVL1Di7sFu8XOouxFrJi0gisnXUlGXEbPA7SGjgYzoB3sHdYiZ7ayRkPaDEgtiAtLAAAgAElEQVSbCemzIG0mh4I5/Ph9LzuPtpOfGsc/rpnNmrlZJ+7SOAJ/oIJBTXGti12HjercX4+24PEHsVsVF+WlhCt083OTztq6X2cq6PHgPXqUrk8/pf5ffgKBANhsZPz9d7Dn5mJ1JGJNSsQSuo6PR43SoHWtNWUNHeFFtfdVtQEwNS2elYWZrJqTxUWTksf0zHFaa9xFxbRv7R/aEq9ZQ8JVV/XrHnm4oYNX99Xw8t4ayhs7sVoUS6ensfaCHFYVZpJ4Po/tOUedbiDRWuPq9oe7JTZ3eGiKqJaFA5kZ0pxdA3dbtFnUoF0Wjds921Ljo3vP1hoMwv5n4c0fQnstzL8Nrt4AiTnDc3LOV1obX3SGukRW7AJvuzHRSM5FMHW5EdomLgTbKQ5LEOOaBDghxAk53U7erX6X7ce2817Ne3T7u3HYHSyduJQVeStYmrOUBFsctB0zglnfsOZu6zlYlMPo7pg2ywhqZlgjJd+Ytho43tLFL7Ye5OW9NaTGR/Gtq2dwx6K8MbMGj9sXYE9FK7sON7HrcCNFNS60BkeMjcumpZqBLp381LhRHz8X6OjAe+QInsNH8JQfwXukHE95Ob6qKuMD11BZLFgcDqwOB9bERCyJiea1A2tiEtZEh3FfOPg5sCYlYXU4sCQlYYk6tcpRIKj55FhrOLRVNhtjHC+clMwqM7RNzxjbSzYMGtoWLyZxzeoBQ9tgxympbeeVfTW8sreGqtZuoqwWrpyVztoLcriqIIO4qPFfCT7fDdQlMNpm4evLpzE3N8kMYD0hLHLSj5ZOL77AwJ/XkuPs4RDWK4D16cKYFh9NYuyJuy0OiacDdv0S3vutMe5q2bdh8TfAfu51Px8xHQ29x7G11xj3p0wxK2zLYcoyY1Zkcd6SACeE6Ke6o5odx3aw/fh2Pqn/hIAOkBGXwfKJV7AiaTYLg1bszeU9Ya2pDPwRXRfj03vGpoWu02eDI3vQafidXV5+s/0wf36/EosF/mbZVL56+dQxP4tYS6eX3WZ1bmdZE9VO4zzkJseyzBw7t2R62oh1f9NaE2huxnOkHG/5ETxHyvEcOYz3SDn+hp4xgcpuJyo/n6hp04ieOhVbRjr1P/0Z2tszEYCKimLi73+HslgJtLsIulwEXO0EXG0EXe0EXKH7XMb2NheB9na0u/8C6pFUdPTAwc/hwJJkBL9gQgIHO+CvTT521bqp8tvxxcQzf3YOq+Zmc3VB5pgfg6i1xn2gyAhtW7cZoc1mMyptpxDaTnT8z447eWVvLZv311Dv8hBrt3JVQQZrL8jhipnpY7r7qOivqcNDSa2Lrz/5CS73AJNM9BFjt5hdE0MBzAxh8VG9uiymJUSREh81el98tRw1Zj4secWYQGr1T2D2dbIMy0C8nVD5vtEt8sgOaDCnkYhNgSlX9HSLTMkfxUaKsUYCnBACrTWlLaXhSUgOth4E+P/s3Xd4VVXWx/HvuTe9JyQhHUiABKSIgAoq0sGCdSyDzrw6Y2+MbcSGHbGLioXRcZrYZhQFREABxUovUkIglHTSe7nlvH/ckEIzSJKbwO/zPDwk5+5zzjoY5K679l6bnj4RjPIMZ0yNjb5FGRhFu5p3sgqOr5/2mNK8subX8n2pamwO/vnDbmYt20FFrZ3LBsdz57jeRAV37Dfrh2KaJrsLq1zVubR8fthZSHmNHcOAk2KCOKNnOGf1jGBI99CjfqNtOp3Yc3Ko3bnzgGRtJ87Sxiqnxc+vIUnzSkrCOykRr8REvOLjMTwaKzU5jz5Gyf/+B7Ym06k8PQn53e+Oei2cs66uIdlzlpXiKC/HUVqGs7wMR2nZYZNBe1kZzrJyDPMI1UDDcFX0DpP4NVT8mlYCGxLFICzerTut6MCmL82Sti8XuaqbHh4NlbbAMWOwhoS0agzgmtq7ancR8zZm88WmXIoq6wj09mD8SVFMGhjNGT3DO0zVWlzrztLzK9maU+b6lVvO1pwy8st/fS+zT28Z3pCcdbpqa/py17YD+7ZAjxEw8Rno2tfdUbmX0wE56xsrbBk/g6POtZQg4XRXspY0CqIGHvf7rclvpwRO5ARld9pZu3c5S3fOZ2neSnJs5RjAIBuMLitmVFU1CXa7a6PPsMTG6Y77pz526QXev30am9NpMnd9Fi8s3k5WSTWjUyK5b2IKyVGBv35yJ2F3ONmUVcp3aQWs2FHAur3F2Bwm3h4WhnYP48xe4ZzZM5y+0UENa7hMm426jAxqd9ZPedy50zUNctcuzOrGKqc1NBSvpES8E5Pw7pmEV6IrWfOI+pV1gvXctVFpRlEVS7bksWRLHit3F+F0OOjmazChmz9nx/pwUoCBpariiIlfY2JY3uzP5FAMb+9DJ377E8ID1/s1nf4ZGHjQ2r/9TV8Cxo/HKy6W8kWL2y1pOxy7w8kPOwuZtyGbLzfnUl5jJ9TPk3P6RzNpQAyn9gjD2oHXCB5viirrGhO1HFeitmNfBXUO1wcVXlYLPSMD6BMdRJ/oQPpEB3H3xxvILT24kn1ctMV32GHNu7D0SagtgyF/hlEPHNUHfZ1e0a7GCtuub6GmxHU8qn99p8hRkDAMvPzcGaV0IkrgRI53pgnluVCQSlXeL/yQ/SNLy3fyjVlBmcXA2+lkWHUNo2vtjPCJpUtEn+bJWlgSeLTu9L/v0gqY/sVWtuSU0T82mPvPTWF4Unir3qMjqqy1s3JXESvSCvh5axY16buIL8+jd20h/R1FxJXm4bMvG+yNVU6P6Oj6alqTZC0pqdPsCWaariYwize7krYtOWWAq6Pn+L5RjD+pK/1jg3/z2huzrq55xW9/sndQFbC+Oth0Kmh5uauBy+EYBpaAgMZqno8P1Rs2NK4ftFrxHz6coIkTCRwzul2TtsOptTv4dnsB8zZks2RLHtU2B5GB3pw3IJpJA2MYFB/i9rWZxwu7w8muAlfH0P2J2rbcMvLKGqtqEYHepEQF0jc6iD7RQaREB5IUEXBQdfSEaItfVQTLn4ZV74BPEIx6EAZfC9ZOVlVsiaoiV6K2P2kr2eM6HhQHSSPr17GdDQGda+sW6TiUwIl0ZEfTVdHpcP0jcUAjkcKiNL6x2lnq78ePPj7UWQyCTYOzvSIY3WUAwxJG4de1P4QkNDQSaStbssuY8eU2vt2eT1yoq8vapAExHbqD4LFylJW5qmjp6c2aidiyslzJNeA0DPYFhJPuH0lGYFdqYuLp2i+Fk4YN4PR+8Z2u46Dd4WTl7qKGpC2rpBrDgCHdQhnfN4pxfbvSPdzf3WFimibOyqom0z6PnPhVb9qEo6B+Y1yrleCLLiTmqafc+xBHUFVnZ+m2fczbkM2y1Hzq7E5iQ3yZNDCGSQOj6RsdpGSuhUqq6tiSU8a2+kRta24Z2/MqqLO7knlPq0FSRAB965O0PvUJW3hAy6fwnjBt8fO2wJf3uRKcyL4wcQYknu3uqI6NvRb2/uSaEpm+DLLXAyZ4B0H3sxrXsXXpqXWA0iqUwIl0VIfb1+y8FyH65MZ2/Pt/L0wDu2sKzh4PD5aFRLA0IID1Ri0mEOMdxui4sxmddD6Dup6Ch6X9PvXMLqnmhcXb+WRdJkE+ntw+uid/GNYNb4/jo+GCaZrY8/NdSVqTqY+16Ttx5Bc0jDO8vPDq0cO1Li0pCe+kJNf6tO7dMTw9SdtXwYo01/q5n3cVUVXnwGLAwPgQzqrvbnlyfAheHh1vXURVnZ1vt+ezeEseS7fto6TKhpeHhRG9whnfN4rRfSKP6s1sR2Pbt4+d48Zj1jZWVwxvb3p+taRTbIBeVmNjyeY85m3M5ru0AuxOk8QIf84fEMMFA6PpGXn8TF0+Fg6nya6CJmvVcsrYlltOTpPpjV38vZpNf0yJCqJnZECH/HvZYZkmbJsPix50ffCYcj6MfxLCerg7spZxOl3NRvZ3i9zzg6uRl8XD1dI/sT5hix18fFYYxe2UwIl0VC/2hbKsXx8XkoCzS282h3RlmdXG0sq97KxytR1OCUthdPxoRieMpndo73b/tL202sYby3fy7ve7MIFrz+jOLWf3JNivc1WU9jOdTmzZ2dTu2FHfkr+xNb+zrKxhnCUgoHHKY1Kia31azyQ8Y2MxrC1LWuvsTtbt3b9dQQEbMkpwmq7Nc09LdG1XcFavcHpGBritilJQUcvXW11VthVpBdTanQT7ejKmj2tT7RG9wztf04XDaM2mL+5WVFnHl7/kMm9DNj/tKsQ0ISUq0FWZGxBDQpcTYx1OaZWNrbn1SVpOOVtzy0jNLae2vqrmYXFV1fpEB5JSX1HrEx1IZGDna7DUYdlq4KdZ8O0LrgZZw2+DM+86pvXVbaY0q3ED7fTlUJnvOh6e3Njev/sZ4K0PQ6TtKYETcSd7LRTvdrXhL9zhqqIV7nR9X1Vw+PMu+Ru2LkmsspeyNOcHlu1dxr7qfVgNK4O7DmZ0gmtT7dgA90y/qbU7+M9Pe3l1aRql1TYuPjmWu8b3Ji60c7wxNOvqqNu7t7Hb446d1KanU7drV7OW+dYuXVxVtKbJWlISHpGRrZ5UlVbb+HFnId/tyOf7HYXsKqgEoGuQt6u7Za9wzkgKJ7KNW+3vLqhk8ZZclmzJY/WeYkzT1Whh//5sQ7uHdpoNzY+Gu5q+tLV9ZTUs2JTDvA3ZrN3raqwwMD6ESQOiOX9ATKfsBnsgh9NkT2Flwzq1/VW1/Vt+AIT6eTZMe9yfqPWMDDhuZgl0eGXZ8NVjsPED13YzYx+D/pe5twtjTZlr4+z9SVvBdtdx/8jGTpGJI7VZubiFEjiRtmaarn+cCuuTtIIdjclayV5o2kLdP9I1Rz68J2z5jAVWGzNDQ8j1sBJld3BjSSn+vmEs7XcuK7JWUGGrwNfDlzNizmB0wmhGxI0g2DvYjY9qMn9jDs8u2kZGUTVn9gxn6jkp9Ittv5gObPN+JM6qKmrTdzW05N+frNVlZDRrJOIZE9PYmr+na+qjd2KiW5tWZBZX8V2aqzr3/Y4CiqtclaHkroGu7pa9wjmtR9gxV8BM02RjZilLtuSxeEsu2/MqAOgbHcT4k7oyrm9XraU6TmQUVbFgUw7zN2bzS1YZhgFDu4cxaWAM5/aLoksnmAJbVmNrWKe2LbeMLTnlbM8tb2gOYrUYJIb7NyRqKdGuBiORgd76Ge4IMlbCwvsge61rKuLEZyBucPvc22GDrDWN7f0zV4HpAE8/6DbcVWFLGuVat6efFXEzJXAiraWm9OAErXCHq6Jmq2oc5+kHXZJciVqXXo0JW1gS+DYmBAuWP8yjuz6lpmmDD9MEwyDMJ4yR8SMZHT+a06JPw8fD/Z+S/5ReyNNfbGVDZikpUYE8cG4fRvRu/7VB+9u8h1x5ZcPUNkdJCbX716ftr6bt3IktO7vxRA8PvBISGqc87l+n1qMHFr+OXTl0Ol2dHl37zxWwcncRdXYnnlaDUxJCGzYUHxAX0qyd/OGaJtTZnfyUXtjQ7j+3rAarxeDU7mGM6+tK2uLDOvafiRyb9PwK5m/M4fMN2ezYV4HVYjA8qQuTBsYw4aQogn3dOw3a6TTZU1TFtvqK2paccrbllpFZ3FhVC/HzpE9UY1ORvtGutWra7LyDczpdlbivHoWKPDj5KlcDr8Co1r2Pabpmu+zvFLn7O6grB8MCMYMa2/vHnwoeHf/DCzmxKIETORr2OteUx6YJ2v6ErXJf4zjDAiHd6pOzXvUJW32yFhjdomkh4z4eR25V7kHHu/h04evLvsbaxh0jWyotr5wZC7fx9bZ9RAf7cPf4ZC4eFOuWfadsefvYOXYsps0GViu+/ftTl5GBo7CwYYzh44NXYo/m69OSEvFKSMDwat3tEtylxuZg1e6ihoRuc7ZrfV6QjwfDk1zVueo6By8uSaXa1lgB9rQa9I8JJm1fBeW1dnw9rZzdO4JxfbsyOiWSUP/j489HWs40TVLzypm3IZt5G3LYW1SFl9XCiN4RTBoYzdg+XfH3btt1juU1NlJzy5ttgJ2aW05VnauqZjGgR5Oq2v7mIlFBPqqqdWa15fDt8/DT62D1ghH3wuk3H1syVbEP0r9pnBa5f515aI/GKZE9RoBv59imRU5cSuBEDrR/37RDTXks3uOaUrGfX/jBCVp4Lwjt/pv/kTFNk/np83nguwcO+bqBwcb/2/ibrt2a8spqePmr7Xy4KgN/Lw9uGdWTa8/o7pZPtx0VlZR+/hn5L89s1kzE2qULASPPxjupZ0NFzTMm5qDNmY93hRW1/LCzsGHKZdO1PweyGHDZ4HjG9e3Kmb3CVa2QBvun087bkM38jTnkltXg42lhTEpXJg2MZmRy5DH9vDidJhnFVc3Wqm3NLSOjqPHnNcjH46BErVdkIL5e+jk9bhXuhMUPQeoXrkRrwnRIPqdl0xjrqmDvD43TIvN+cR33DXXtw5Y40pW4hXZvu/hF2oASODlx1ZQ1TnFsmO5Y/31dReM4D98mUx73V9R6uo618qd0Wwu38vTKp1m3bx2eFk9sTttBY6L9o1n8u8Wtet+jUVFrZ/Y3O/nbil3YnU7+cHp3bhvdkzA3VGdqd+6k+L05lH72Gc7KStc/6E3+v9WZ2ry3F9N0tUkf/cI3h3zdAHbNOK99g5JOx+k0Wb2nmHkbsvliUw6FlXUEeHswvm9XJg2M4cxe4SzYmHPYfc0qa+1sy21cq7Y1p5xtOWVU1lfVDAN6dPFv3q4/OoiYYFXVTlg7voYv73dtnZM02pWArXq7+T6p/S6FnPWN7f0zfgZHnauCl3B6Y3v/6IFtvu+pSFtSAifHN4fNVTVrqKalNSZsFXlNBhqujawbkrMmyVpgTJt3wiqpKeHVda/y8faPCfUJZcopU/C0ePL4j49T42jseuhj9eHR4Y9yXmL7v8G2OZx8sHIvL3+VRmFlHZMGxnDv+OR2bzlu2u2UL11K8Zz3qfrpJwxPT4LOPQdHVTUVy5cfF23e28MZM5YeshIXG+LL91NHuyEi6azsDic/phcyb0M2X/6SS1mNHV9PC3UOE4ez8X2Eh8Wgb3QgpTV29hQ2rgsO9PZoSNT2t+tP7qqqmhyCwwar3nGtj7Mf8P8vw+qa+bJ/zXnX/pA00pW0JQwDL63blePHkRK442PzHjn+maYrGWtI0HY0/ire7dpbZj+/Lq7ErOc4VwVtf8IW2gM8278RiMPp4L/b/8ur61+loq6CyX0mc8vJtxDkFQSAxbAwc+1McitzifKPYsopU9o9eTNNk0Wb83j2y22kF1Ryao8w3jm3DyfHt28HRntBASUff0zxhx9hz83FIyaaiDvvJOR3l+LRpQvpF13cPHkDsNmoXreuXePsLO6dkMz9n2xq6M4H4Otp5d4JyW6MSjojD6uFs3pFcFavCJ64qB8rthdw+/vrcDidzcbZnSabc8oZ37crl54SV78JdiBxob6qqknLWD3h9Jvgh5lQdkACZzoAAy59x7WOLSDSLSGKuJsqcNKx1FY0T86aVtTqyhvHefi4OjqG92ze6bFLEviFuS/+A6zNW8vTK59mW9E2hkYN5f5T76dXaC93h9XMmj1FTP9iG2v2FNMzMoD7z0lhdErr73F2OKZpUr1uPcVz5lC2aBHYbPgPH07oVZMJGDmyxZtiy6EdrgulyLHqMXUBh3oHoSm60ioeDYHD/YQ9WtLe0Yi0O1XgpH1t/Ai+frz5nPUBlze+7rBDyZ4DErT6r8tzmlzIgJB4V2IWf1pjK/4uPSEozr2bf/6K/Kp8XlzzIvPT59PVryvPnf0cE7pN6FCfQKfnV/Dsl6l8uTmXiEBvnr6kP5cNjmu3DZqd1dWUzp9P8Zz3qd26FUtAAKFXXkno73+Pd2KPdonhRHDRoFglbNImYkJ8DzlFNybE1w3RyHEnOA5KMw59XOQEpwROWtfGj2DeHWCr/0e9NAM+uwU2fQwWD1eyVryr+ZRH31BXBS1xVPOKWlgP8OxcbwRsDhv/2fof3tzwJjanjev7X891/a/Dz7PjzMvPL6/lla/TmLNyLz4eFu4a15vrzupxzJtBt1Tdnj0Uv/8BJZ98grOsDO/evYl69FGCJ52Pxd+/XWIQkWOnKbrSpsZMa/5+AlzvCcZovbOIEjhpXV8/3vx/tuBakJy2GCL7QmQK9JnUvNNjB5ryeCy+z/qeGStnsLtsNyPjRvLXoX8lPije3WE1qKqz8/aKXbz1zU5q7E4mn5rAHWN6ERHY9puXmg4HFd9+S/Gc96lcsQI8PAgcN5awq67Cd/DgDlWZFJGW2V/Z1RRdaRP7Z+4caUaPyAlKa+CkdZ2Ac9YzyzN5dtWzLMtYRkJgAvedeh8j4ka4O6wGdoeT/67J5MUl29lXXsvEk6K4d2IySREBbX/v4mJKP/mE4vc/wJaZiUdEBCFXXEHI5ZfhGanF5yIiIiKHojVw0n4CIg9o3V/vOJyzXm2v5p1N7/DuL+9itViZcsoU/tj3j3hZ23+vtEMxTZOl2/YxY+E20vZVcEpCCK9fdQpDurd9xbN60y+upiRffIFZW4vfkCFE3nM3gWPGYHh6tvn9RURERI5XSuCkdQVEYVbk0XRCnN3qg8dxNGfdNE2+2vsVz616jpzKHM7pcQ53Db6LKP8od4fWYENGCU8v3MpP6UX0CPfnzatPYcJJUW06VdFZW0v5l19S9N4cajZuxPDzI/jiiwj9/WR8knu32X1FRERETiRK4KT17P0Zcjew0Hk6A9hBjFFIttmFl51XcqbjDC5yd3ytYGfJTp5e+TQ/5/xM79DeTD9zOkOiDlnddou9hVU8tziVeRuy6eLvxRMXnsSVpybg2YadJW1ZWRR/8CEl//0vjuJivHr0oOuDDxJ80YVYAwPb7L4iIiIiJyIlcNI6TBO+foxCQri77gaqab5h9pdzN1FWYyM+1I/4MF/iQv3w8ew8+3uV15XzxoY3eH/r+/h6+vLAaQ9wWe/L8LB0jL9CxZV1vLp0B//+aTdWi8Hto3tyw4hEAn3aZrqi6XRS+cOPFM+ZQ8Xy5QAEjB5F2OTJ+A0bpqYkIiIiIm2kY7z7lM5vx9ew53tm2q45KHkDqKh1MO2zzc2ORQR6Ex/qS3yYX0Ni5/rdj+hgn3bbj+xInKaTeTvn8dKalyiqKeKSXpdwxyl3EObTMTpn1tgcvPv9bl5fvoPKWjuXD4nnznG96Rp08H+D1uAoK6N07lyK57xP3e7dWMPC6HL99YRecTmeMTFtck8RERERaaQETo6d0wlfPwYh3VhQMh4cBw+JDfHh01vPIKOomsziKjKKqsgoqiajuIq1e4uZvzEHh7Oxe6XVYhAd7NMssYtrkuBFBHhjsbRtlWdz4Wam/zydjfkbGRAxgFljZnFS+Eltes+WcjhN5q7L4oXFqWSX1jAmJZL7zkmhd9e2mbJYk5pK8XtzKJ03D7O6Gt+BA4l59hkCJ07E4tUxmraIiIiInAiUwMmx2/Ip5G6kdOIsKuYZWAyTJrlY/cauKUQG+hAZ6MPgbqEHXcLucJJTWkNGcRWZ9YldRlEVGcXVLE/NZ195bbPx3h4WYkN9D6rc7f8+2NfzN0/jK64pZubamXyS9gmhPqE8ccYTXJB0ARbD/RVBgG+35/P0wm1szSljQFwwz18+kOFJ4a1+H7OujvKvvqJozhyqV6/B8PYm6PzzCJ08Gd+TOkYiKyIiInKiUQInx8Zhg6VPQWRfHtqZjGkUcP85Kfzjh91HtbGrh9XiSsDC/CDp4NdrbA4yi6vrEzxXYudK8KpYn1FCabWt2fhAbw/iwvyaTNH0bbh+XKgvfl4H/+jbnXY+Sv2I19a/RrWtmqv7Xs3NA28m0KtjNOLYnF3KjIXbWJFWQHyYL6/8fhDn949u9UqkLS+Pkg8/ovjjj3DkF+AZH0/kvfcScuklWENCWvVeIiIiInJ0lMDJsVn3HyjayS8j3mLe4n3cObY3149I5PoRia16Gx9PKz0jA+gZeejNp8tqbA3TMjObVO92FVTybVo+NTZns/HhAV7ENVTtfDF90lmW/xZZVemcGnUaD5x2P0khh8gk3SCrpJoXFqXy6fosgn09efj8vlx9egLeHq3XBMY0TapWraL4vTmUf/UVOJ34jziLsMmT8T/rLAxLx6g+ioiIiJzoDNM0f31UOxoyZIi5evVqd4chLWGrhlcG4QyKY2TRA3h4WFg45axWTSxag2maFFTUNUzLzGxSvdtTkkOh9yd4BK3HaQuhNu88nBX9iA52VeqaNVip/zoysO3X3wGUVtt4ffkO3v1+NwB/OqMHN49MIti39TpLOioqKZv3OcVz5lCbtgNLcDAhl15K6JVX4JWQ0Gr3EREREZGWMwxjjWmah9yrShU4+e1WzobyHD5MeIS9O6uZc/1pHS55AzAMg4hAbyICvTklwbX+rs5Rx7+2/IvZG2fj53Twu55/ZliXy9hX5mw2RXNFWj55Zc3X33l5WIgL8W02RTMutHEdXqjfb19/B1Brd/DvH/fw2rIdlFbbuHhQLHePTyY2xPeY/hya3SM93dWUZO5cnJWV+PTtS/RTTxJ07rlYfFvvPiIiIiLSulqUwBmGMRGYCViBt03TnHHA63cB1wF2IB/4k2mae+pfcwCb6ofuNU3zglaKXdypugRWvEhl/EimrQ/mklNi2qSRRlv4NvNbnl31LHvK9jAqfhT3Dr2X+MD4w46vsTnIKqlumJaZWV+9yyiqZmNmCSVVzdffBXh7EBfqWz9Fs0mDlfqv/b2b/7Wbuy6L5xalkl1STYifq7pWXGXjrF7hTD0nhZNiglvluU27nfJlyyieM4eqH3/C8PQk8JyJhE2ejM/Agdq7TURERKQT+NUEzjAMKzALGAdkAqsMw/jcNM0tTYatA4aYplllGMbNwLPAFfWvVZumeXIrxy3u9sOrUFPCY1W/w9/bgwfP7ePuiH5VRlkGz6x6hm8yv6F7UHfeHPsmZ8Se8avn+XhaSYoIICni0OvvymtsDSxe6PcAACAASURBVFsiNJ2iubeoku93FFBta76vQpi/F/GhrgpeTZ2Db9PysTlcU5mLq2wYwE1nJzL1nNb5M7UXFlLy8ccUf/gR9pwcPKKjifjLXwi57Hd4dOnSKvcQERERkfbRkgrcqcAO0zTTAQzD+AC4EGhI4EzTXNZk/E/A1a0ZpHQwFfvgp9fZGz2Rj3aF8cylKXQJ8HZ3VIdVZavi7U1v84/N/8DT4sldg+/i6j5X42ltnbVkgT6e9I3xpG9M0EGvmaZJYWVds3V3+xutbM4qZXdh1cHnAPM25BxTAmeaJtXr11M8533KvvwSbDb8hw8j6sEHCBg5EsNDs6dFREREOqOWvIuLBTKafJ8JnHaE8X8GFjb53scwjNW4plfOME1z7oEnGIZxA3ADQIIaJ3R83z6Haa/lttxzGdo9lMsGH376oTuZpsmiPYt4YfUL5Fbmcn7i+dw5+E4i/SLbLQbDMAgP8CY8wJuT4w9uwd9j6gIO1UYou6T6N93PWV1N2YIFFM2ZQ+2WrVgCAgi94gpCJ/8e78TW7QwqIiIiIu2vVT+GNwzjamAIcHaTw91M08wyDCMRWGoYxibTNHc2Pc80zdnAbHB1oWzNmKSVFe+G1e/yU/C5bNkXwRcX92+XjoxHK604jRkrZ7AydyUpYSk8O+JZBkUOcndYB4kJ8SXrEMlazFE2LKnbu5fi9z+g5JNPcJaW4t2rF1GPPkLwpElY/P1bK1wRERERcbOWJHBZQNMSS1z9sWYMwxgLPAicbZpmQ9s+0zSz6n9PNwxjOTAI2Hng+dJJLHsap2HhL7kTuGFkIr27doxNrvcrqyvjjfVv8P629wnwCuDh0x/m0l6XYrV0vO6YAPdOSOb+TzY1Wyfn62nl3gnJv3qu6XBQsWIFxXPmULniO7BaCRw7lrCrJuM7ZIiakoiIiIgch1qSwK0CehmG0QNX4nYlMLnpAMMwBgFvARNN09zX5HgoUGWaZq1hGOHAGbganEhnlLcFc+OHfOR5EV5hsdw+upe7I2rgNJ18tuMzXl77MsU1xVzW+zJuH3Q7IT4HT1vsSC4aFAvQ0IUyJsSXeyckNxw/FHtxMaWffELx+x9gy8zEIyKC8FtuIeTyy/Hs2n7TQ0VERESk/f1qAmeapt0wjNuARbi2Efi7aZqbDcN4HFhtmubnwHNAAPBx/af++7cL6AO8ZRiGE7DgWgO35ZA3ko5v6RPUWf15uvwcZl7bD1+vjlHV2pS/iadXPs2mgk2cHHEyb459kz5dOn5XzP0uGhR7xIRtv+pfNlM8Zw5lCxZg1tbiN2QIkXffReDYsRierbe5t4iIiIh0XC1aA2ea5hfAFwccm9bk67GHOe8HoP+xBCgdRMZKSP2CWY4rOHNAL0Ymu7/SU1hdyMy1M/l0x6eE+4Yz/czpnJ94/nE1ddBZV0f5woUUzZlDzYaNGL6+BF90EaGTJ+OT3Nvd4YmIiIhIO1Mvcfl1pon51aOUWkL5wDyP+ef3dWs4dqedD7Z9wOvrX6faXs01J13DjQNuJMDr0Pu0dQa2ffvIuutu4l56EY+ICGzZ2RR/8CEl//0vjqIivLp3p+sDDxB88UVYAzvWukMRERERaT9K4OTX7fwaY8/3vGj7P247fyCRQT5uC2VV7iqm/zydHSU7GB4znPtOvY/E4M7fHr/g9TeoXrOG7GnTMCxWKpa5tlYMGDWK0Mm/x3/YMAyLxc1RioiIiIi7KYGTI3M6cSx+lFwi2Rx1MY+c1s0tYeRW5vL86udZtHsRsQGxvDzqZUbHj+700yUd5eVU/vQzJf/9L5gmlcuWYwkOpst11xF6xeV4xv762jgREREROXEogZMj2zIX675NvGC7mccuOQVrO+/5Vuuo5Z+b/8nbm97GaTq55eRbuPaka/HxcF8V8LcwnU5se/dSsy2V2u2prt+3bcOWnd18oNVK4MQJRN51p3sCFREREZEOTQmcHJ7DRs3ix9ntjCfs9KvoFxvcrrf/JuMbnln1DBnlGYxNGMs9Q+8hNqDjV6QcFRXUbt9OzbZt1G5LpTY1lZq0NMyqKtcAiwWvHj3wPXkggeedR9E//gE2W/3JDsrmfkbkbbfhERHhtmcQERERkY5JCZwcln3tf/Ap28U7XlN5dHz7teXfU7aHZ1Y+w4qsFSQGJzJ73GyGxQxrt/u3lOl0YsvMdCVqqdupSXUlbLbMzIYxlqAgfJKTCbn0UnxSkvHunYx3r55YfFwVxJxHHzvkdfNff4PoR6Yd9JqIiIiInNiUwMmh2aqpXfIUG5y9GHvRNfh7t/2PSpWtitkbZ/OvLf/Cy+rFPUPuYXKfyXha3L/HmaOiktq07a5qWn3CVpuainN/Vc0w8OreHZ/+/Qj53aV4Jyfjk5KCR1TUEdfpVa9f31h9289mo3rdujZ8GhERERHprJTAySGVfPM6IXX5fB1zP3/tF92m9zJNk4W7FvLCmhfYV7WPC5Iu4M7BdxLuG96m9z1cLLasLGq3baMmNZXabanUpKZi27u3YYwlMBDv5N4EX3wx3inJ+CQn492rFxZf36O+X+LcT1szfBERERE5zimBk4OY1SV4/PASK8yTuerKyW16r9SiVJ5e+TRr8tbQJ6wPL5z9AidHntym99zPWVVFbVqaq6FI6rb6BiPbcVZUuAYYBl4JCfj06UPIxRe5qmrJyXjExHT67pciIiIi0jkpgZOD7PxsBj2d5eSfeh9nhRx9VaklSmtLmbV+Fh+mfkiQVxDThk3jkp6XYLVYW/1epmliz852VdRSGztA1u3dC6YJgMXfH+/kZIIvmIR3coprvVqvXlj8/Fo9HhERERGR30oJnDRTWZRNzLZ3+cbzLC6YOLHVr+9wOvh0x6e8svYVSutKubz35dw26DaCvVunw6WzupraHTsaOkDWpLrWqznLyxvGeCYk4JOcTNAFk1zTH1NS8IyNVVVNRERERDo8JXDSzC/vT2OwWUfEBY/jYbW06rU35G9g+s/T2VK4hVMiT+GB0x4gOSz5N13LNE3subn1DUVSG9ar1e3ZA04nABY/P7yTkwk671x8UlLwTk7Gu1dvrAH+rflYIiIiIiLtRgmcNEjduolB+z5hbZfzObX/Ka123YLqAl5a8xKf7/ycSN9IZpw1g3N7nNviipezpobatB3NNsCu2b4dZ2lpwxjPuDi8U5IJOvdcvJN745OSgmdcHIaldZNQERERERF3UgInADicJllzH6GbYSXlyqeO6VoL0hcwc+1McitzCfQKpNZeiwMHf+r3J24ccCN+nodeV2aaJvZ9+1wJ2v4NsFNTqdu1q6GqZvj54dOrF0ETJrg6QKak4N27N9aAgGOKWURERESkM1ACJwDMX/IVk2qWsrPXtfSKTPjN11mQvoBHf3gUn9JqHpnr4KWLSrEFWrnzlDu5pt81DeOctbXU7thRv59aY8LmKClpGOMZE4N3SgpBE8a7Gosk98YzIUFVNRERERE5YSmBE/LKagj4YQY1Fl96XvzQMV1r5tqZ1DhquOo7JymZcOl3Dj45E9Z+/g7n/2hrSNhq03eBwwGA4eODd+/eBI4bV78BdjLeyclYAwNb4/FERERERI4bSuCEf3/0MfcYqyk+7T78/Lsc07VyKnOIKHEydoOJxYQJ62DiOgdQQD4v4hETjU/vZALGjKmf/piMV7cEDGvrbx8gIiIiInK8UQJ3glu2LY8z986iyjuM0FF3HNO1Fu1eBKbJgx84sbqWrOEEtsfCkokRvHbDPKzBrbNdgIiIiIjIiUiLiU5g1XUO5n3yH063bMVz9F/B+7c3AlmesZyp307l+p+DiCluPG4FkvLgwlE3K3kTERERETlGSuBOYK98ncq1Nf+iJiAOz6F/+s3X+SH7B+5afhfn7Yth3LJizAN2B/DAysAFaccYrYiIiIiIaArlCSo1t5ys79+nv8duGPsmeHj/puuszl3NlKVTGFobw9Uf5oG3N9TWNhtjsTuoXreuFaIWERERETmxKYE7ATmdJtM+WcezHh9jD0/BY8Dlv+k6G/M3cuvXt9LDI5K7/2sDD096zJ2LZ2xsK0csIiIiIiKgBO6E9NHqDHpkfUY3zxwY+wJYjr4D5Laibdz01U2Ee4cxfWEEtsw1dHv370reRERERETakBK4E0xBRS0vfrGBhd5zMWOGYiSfe9TX2FmykxsW34C/pz+vpJ5K3fcfEfXoI/gNHdoGEYuIiIiIyH5K4E4w0xds5RLHQrpQAGP+AYbxq+c0tadsD9ctvg4PiwdvOSZT++9nCfn9lYReeWXbBCwiIiIiIg2UwJ1AfthRwJJ1aawMmAfdxkCPs47q/OyKbK5bfB0Op4N3Eu6n7qap+A0dStQDD7RRxCIiIiIi0pQSuBNErd3BQ3N/4Z7ARfjaymDMtKM6f1/VPq5bfB2VtkreGfw8XHc/HuHhxM58GcPTs42iFhERERGRppTAnSDeWL6TsoJsrg5YACddDDEnt/jcwupCrlt8HYXVhfzt7Fn43v08NeXldH9/Dh5hYW0YtYiIiIiINKUE7gSQnl/B68t2MjtqCdbSWhj1UIvPLa0t5cYlN5JTkcMbY14n/JVPKN2wgdiZM/FJSWnDqEVERERE5EAWdwcgbcs0TR6a+ws9PAs4u2weDLoawnu26NyKugpuWnIT6aXpzBw9kx6LNlP66aeE33ILQRPGt3HkIiIiIiJyIFXgjnNz12fxw85Cvk5chJFngbPva9F5VbYqbv36VrYVbeOlUS8xIN0k49nnCBw3lvDbbm3jqEVERERE5FCUwB3HSqrqeHL+Vi6ILiExez4Mvw2Cf32j7VpHLXcsu4P1+et5dsSzDHd0Z9ddV+DdsycxM2ZgWFS4FRERERFxhxa9EzcMY6JhGKmGYewwDGPqIV6/yzCMLYZhbDQM42vDMLo1ee3/DMNIq//1f60ZvBzZM19uo6TaxpNBczG8A+HMu371HJvDxl3L72JlzkqePONJxoafQcatt2FYLMS9PguLv387RC4iIiIiIofyqwmcYRhWYBZwDtAX+L1hGH0PGLYOGGKa5gDgv8Cz9eeGAY8ApwGnAo8YhhHaeuHL4azeXcT7KzN4aGAFQXsWw/A7wO/IHSPtTjv3rbiPbzO/5aHTH+L87ueSffc91O3eTezLL+MVF9dO0YuIiIiIyKG0pAJ3KrDDNM100zTrgA+AC5sOME1zmWmaVfXf/gTsf6c/AVhimmaRaZrFwBJgYuuELodjczh58NNfiAny5o+V/wT/CDj95iOe4zSdPPz9wyzZs4S/Dv0rlydfTv7LM6n45hu6PnA//qef1k7Ri4iIiIjI4bQkgYsFMpp8n1l/7HD+DCw8mnMNw7jBMIzVhmGszs/Pb0FIciRvr9hFal45r55einXvdzDiXvAOOOx40zR5/MfHmZ8+nzsG3cEf+v6B0vkLKPzb3wi5/HJCJ09ux+hFRERERORwWrUbhWEYVwNDgOeO5jzTNGebpjnENM0hERERrRnSCSejqIqZX29nQp8IBqfNhOAEGHzNYcebpskzq57hf2n/4/r+13P9gOup/mUzOQ8+iO+QwUQ99CCGYbTfA4iIiIiIyGG1JIHLAuKbfB9Xf6wZwzDGAg8CF5imWXs050rrME2TaZ/9gtUweLrPLsjZAKMeAA/vw46fuXYm7219jz/0/QO3D7ode34+mbfdhrVLGHEzZ2J4ebXzU4iIiIiIyOG0JIFbBfQyDKOHYRhewJXA500HGIYxCHgLV/K2r8lLi4DxhmGE1jcvGV9/TNrAwl9yWZaaz11jEgn7+VmI6AMDLj/s+NkbZ/POL+9wee/LuXfIvZg2G5l3TMFRUkL8rFl4dOnSjtGLiIiIiMiv+dV94EzTtBuGcRuuxMsK/N00zc2GYTwOrDZN83NcUyYDgI/rp9vtNU3zAtM0iwzDeAJXEgjwuGmaRW3yJCe48hobj83bTN/oIK7x/wEKd8CVc8BiPeT4f27+J6+tf40Lki7gwdMfBCD3sceoXreO2JdexKdPn/YMX0REREREWqBFG3mbpvkF8MUBx6Y1+XrsEc79O/D33xqgtMwLi7ezr7yW2b/vh/WTGyBuKCSfe8ixH277kOdXP8/4buN5bPhjWAwLRf/6N6X/+4QuN99E0DnntHP0IiIiIiLSEi1K4KRj25hZwj9/3M0fTu/GwJyPoTwbLpkNh2g+8tmOz3jy5ycZGTeSGWfNwMPiQeWPP5L3zDMEjBlDxO23t/8DiIiIiIhIi7RqF0ppf3aHkwc+3UREgDf3nB0NK16ApNHQ46yDxn6560um/TCNYdHDeH7k83haPanbu5fMv9yJd2IPYp55BsOiHwkRERERkY5K79Y7uX/9uIdfssqYNqkvQWvfhOpiGDPtoHFL9y7l/hX3MyhyEDNHz8Tb6o2jopLMW2/FAOJmzcIa4N/+DyAiIiIiIi2mKZSdWE5pNS8sTuXs3hGcl+gB82ZB34sgZlCzcd9nfc8939xD3y59mTVmFr4evphOJ9l//Su16btIePtveCUkuOkpRERERESkpVSB68Qe+3wLdqfJExf2w1jxAthrYPRDzcasyl3FlGVTSApJ4vWxr+Pv6aqy5b/yChVLl9J16lT8hw1zR/giIiIiInKUlMB1Ul9vzePLzbncMaYXCZZ8WP13GHQVhPdqGLMhfwO3fn0rcQFxvDXuLYK9gwEoW7iQwjffIvh3lxJ69VXuegQRERERETlKSuA6oao6O9M+20yvyACuPysRls8ADDh7asOYLYVbuHnJzUT4RvC38X8jzCcMgJotW8i+/wF8Bw0iato0jEN0qhQRERERkY5JCVwnNPOrNLJKqpl+SX+8ilJh4wdw6vUQHAvAjuId3LjkRgK8Anh7/NtE+EUAYC8sJOPW27CGhBD36itYvLzc+RgiIiIiInKU1MSkk9maU8bb3+3iiiHxDO0eBh/cDl4BcNbdAOwu3c11i6/D0+LJO+PfITogGgCzro7MO6bgKCqi23vv4REe7s7HEBERERGR30AVuE7E6TR54NNNBPt6MvWcFMhcDdvmw/DbwS+MrIosrlt8HSYmb49/m/igeABM0yT3iSepXrOG6KeewrffSW5+EhERERER+S2UwHUi76/ay7q9JTx4bh9C/Tzhq0fBLxxOv4W8yjz+vOjPVNurmT1uNokhiQ3nFc+ZQ8nHH9PlhhsIPv889z2AiIiIiIgcEyVwnUR+eS3PLNzGsMQuXHJKLKQvg90rYMS9FDhruG7xdZTUlvDWuLdIDktuOK/yp5/Jm/40ASNHEvGXKW58AhEREREROVZaA9dJPLlgCzU2J09e3A8D4KvHIDiBkv4Xc8OSG8iryuPNsW/SL7xfwzl1mZlk/eUveHXvTszzz2FYlK+LiIiIiHRmekffCaxIy+ez9dncNDKJpIgA2PIZ5KynfMSd3LRsCntK9zBz1ExO6XpKwznOykoyb7kV0+kkftZrWAMC3PgEIiIiIiLSGlSB6+BqbA4envsL3bv4ccvIJHDYYemTVEUkc0veUlKLU5k5aibDYoY1nGM6nWRPnUrtjh3Ez56NV/fu7nsAERERERFpNarAdXCvL9vB7sIqnryoPz6eVtgwh5qiHdwRE8PGgk08O+JZRsSNaHZOwWuzKF/yFV3v+ysBZ57hpshFRERERKS1qQLXge3YV8Eb3+zkwpNjOLNXONhqqFs+gzsTklhZls5TZz7FuG7jmp1TtmgxBa+/TvDFFxP6xz+6KXIREREREWkLSuA6KNM0efDTTfh6WnnovL4A2FfO5q8+tXxnsfLIsEeYlDSp2Tk127aRPXUqvgMHEvXYoxiG4Y7QRURERESkjWgKZQf1v7VZ/LyriPvOSSEi0BtHVTEPbnqdr/39mHrqVH7X+3fNxtuLisi85VasQUHEvvoKFi8vN0UuIiIiIiJtRRW4Dqi4so7pX2zllIQQfj80Aafp5PEvruELX0+m9LyMq/pc1Wy8abORNeUv2AsK6Pbef/CMjHRT5CIiIiIi0paUwHVATy/cSmm1jacu7o9hwIzvH+WTynRu9IjiujOmHTQ+d/p0qlatIua5Z/Ht398NEYuIiIiISHvQFMoOZuWuIj5ancl1Z/YgJSqQl9a+xJydn/J/peXcOn7WQeOLP/iAkvc/oMt1fyZ40qRDXFFERERERI4XSuA6kDq7kwc+3URsiC9TxvbizY1v8u4v73JFeRV3dzsfI6J3s/GVK1eS++RT+J89gog773RT1CIiIiIi0l6UwHUgf1uRzo59FTx+4Ul8uP3fvL7+dS707MoDxeUYI+9vNrYuM4usKX/BKz6e2Oefx7Ba3RS1iIiIiIi0FyVwHcSewkpe+TqNiSdFsc9YyotrXmRi9HAeS1uD5dTrITi2YayzqorMW2/FtNuJe30W1sBAN0YuIiIiIiLtRU1MOgDTNHn4s814WAxOG7iD6T9PZ1T8KKbnFWD19Icz72oc63SSPfV+atPSiH/rLbx79HBj5CIiIiIi0p5UgesA5m/M4dvt+Zw3LJeX1j3F8JjhPN/z93imLoAz7gD/Lg1jC954g/LFi4m85x4CzjrTjVGLiIiIiEh7UwXOzUqrbTw+fwuJ3dJZnP8Og7sO5uWRL+H13uXgFw6n39wwtmzJEgpefY3gCy8g7Npr3Be0iIiIiIi4hRI4N3t+USol5kZs/v/mpPCTeG3Ma/ju/Ql2r4CJz4C3a31bTep2su+bis+AAUQ9/jiGYbg5chERERERaW+aQulG6zNKeH/TUvzi/0Pv0F68MfYN/D384KvHIDgBhlwLgL24mMxbb8Xq70/cq69i8fZ2c+QiIiIiIuIOqsC5id3h5O7P5uIb90+6BSXw1ri3CPIKgs1zIWc9XPQGeHhj2mxk/eVO7Pv20e3f/8Kza6S7QxcRERERETdRAucmzyz7ijy/14jwjeDvE98m1CcUHHZY+iREpMCAKwDIm/EMVT//TPSMp/EdONDNUYuIiIiIiDu1aAqlYRgTDcNINQxjh2EYUw/x+gjDMNYahmE3DON3B7zmMAxjff2vz1sr8M7su72beH/vw3hbAphz/ruE+4a7XtgwBwrTYPTDYLFS/NFHFL/3HmHXXkvIRRe5N2gREREREXG7X63AGYZhBWYB44BMYJVhGJ+bprmlybC9wDXAPYe4RLVpmie3QqzHhV2lu7hj2U1gevDGmL8RHRDtesFWA8tnQOwQSDmPqjVryH3iSfzPPJPIe+52b9AiIiIiItIhtGQK5anADtM00wEMw/gAuBBoSOBM09xd/5qzDWI8bmSWZ/KHBX+i1u7kqoQZnBrfq/HFVW9DWRZc/Ca2nBwyb78Dr5gYYl94HsNqdV/QIiIiIiLSYbRkCmUskNHk+8z6Yy3lYxjGasMwfjIM45DzAA3DuKF+zOr8/PyjuHTnkVuZy58W/ZnS2iq6Vt7BvaPPanyxpgxWvACJo3BGDSXjttsw6+qIe+N1rMHB7gtaREREREQ6lPZoYtLNNM0swzASgaWGYWwyTXNn0wGmac4GZgMMGTLEbIeY2lVBdQHXL76egqpiKvf8mX9cOwFPa5Pc+cfXoLoIc/TD5Dz4ILVbtxH/5ht4Jya6L2gREREREelwWlKBywLim3wfV3+sRUzTzKr/PR1YDgw6ivg6veKaYq5ffD05lXmU776GKwYMY3C3sMYBFfnw4yzoeyGF81dT9sVCIu++i4Czz3Zf0CIiIiIi0iG1JIFbBfQyDKOHYRhewJVAi7pJGoYRahiGd/3X4cAZNFk7d7wrqyvjxiU3srdsL2EVNxJk9OK+iSnNB614AWxVlHuOI//llwmaNImwP//ZPQGLiIiIiEiH9qsJnGmaduA2YBGwFfjINM3NhmE8bhjGBQCGYQw1DCMTuAx4yzCMzfWn9wFWG4axAVgGzDige+Vxq9JWyc1f3UxaSRqTou5n++4oHjq/DyF+Xo2DSvbC6neojbqQ7CdexqdfP6KfeBzDMNwXuIiIiIiIdFgtWgNnmuYXwBcHHJvW5OtVuKZWHnjeD0D/Y4yx06mx13D70tvZXLCZh4dO59EPrJzRM5iLTj6g98vyGThqLWR8nIHh50fca69i8fFxT9AiIiIiItLhtWgjb2m5Okcdf1n+F1bnruapM59i2booau1OnriwX/PK2r5tmOveJ2tjCva8fOJefQXPqCj3BS4iIiIiIh2eErhWZHPauPebe/k+63seG/4YAfZTmb8xh1tGJZEYEdB88NInyNsYRuX2fUQ99hh+g06o3i4iIiIiIvIbKIFrJQ6ngwdXPMjSjKXcf+r9nNP9Ah6e+wuJ4f7cPDKp+eDMNZR88TXFW70I+78/EnLJxe4JWkREREREOhUlcK3AaTp55IdHWLh7IXcNvovJfSbz6tI09hZV8eTF/fD2sDYbX/XP+8hZHYL/6acRee+9bopaREREREQ6GyVwx8g0Tab/PJ3Pdn7GzQNv5tp+15KWV87sb9O5ZFAsw5PCm423/fQ/Mj/ei2d4CLEvv4zh0R57qYuIiIiIyPFACdwxME2TF1a/wIepH3LtSddy88CbcTpNHvz0F/y8PHjgvD7Nxjurq8m871FMh5X4v/0da0iImyIXEREREZHOSAncMXh9w+v8c8s/uTL5Su4cfCeGYfDfNZms3F3E/eekEB7g3TDWNE1y7riWmjwbMVMuxzu5rxsjFxERERGRzkgJ3G/0zqZ3eHPDm1zc82LuP+1+DMOgsKKW6Qu3MqRbKJcPiW82vnD2bMpWbCDidG8C/zTtMFcVERERERE5PC3A+g3e2/oeL699mXN6nMMjwx7BYrjy4OlfbKOixs70S/pjsTTu+Va+bBn5L79MUEIVXf76DFish7u0iIiIiIjIYakCd5T+t/1/zFg5gzEJY3jqzKew1idjP+4s5H9rM7l+RCK9uwY2jK/duZPse+7FpwtEX5SI0WeSu0IXEREREZFOThW4X7EgqjZLLwAAF9dJREFUfQEz184ktzKXYO9gSmpLOCP2DJ4d8SyeFk8Aau0OHpy7ifgwX+4Y3avhXEdpKRm33IJhdRI3LBfLxDfBMA53KxERERERkSNSAncEC9IX8OgPj1LjqAGgpLYECxYmdJuAl9WrYdzsb9JJz6/k3WuH4uvlqsiZdjtZd92NLSubbuMr8ew/AhLPdstziIiIiIjI8UFTKI9g5tqZDcnbfk6cvLHhjYbvdxdU8uqyHZzXP5pRyZENx/c9/wKV339P9JVD8QsqhDFqXCIiIiIiIsdGCdwR5FbmHvG4aZo8/NkveFstTJvUuC1AyadzKfrHPwi94lJCLIugzwUQO7hdYhYRERERkeOXErgjiPKPOuLxzzdksyKtgHsmJNM1yAeA6vXryZ02Db/TT6frqTawVcHoh9stZhEREREROX4pgTuCKadMwcfq0+yYj9WHKadMobTaxhPztzIgLpirT+8GgC0vj4zbb8cjKorYx+7GWPt3OHkyRPR2R/giIiIiInKcUROTIzgv8TyAhi6UUf5RTDllCuclnseDn26iqLKWf1w7FKvFwFlTQ+Ztt2NWVhH3zjt4rJsJGHD2VPc+hIiIiIiIHDeUwP2K8xLPa0jk9lu7t5g5K/dyzfDu9IsNxjRNcqZNo2bTJuJmvYZPiBM2zIHTboaQeDdFLiIiIiIixxslcEfJ5nDywCeb6Brow93jkwEo+vu7lH0+j4gpdxA4Zgx8eDV4+sNZd7k5WhEREREROZ5oDdxRevf7XWzLLefRC/oS4O1Bxbffsu/55wmcOJEuN90EmWtg6zwYfhv4h7s7XBEREREROY4ogTsKmcVVvLQkjTEpkUw4KYra9F1k3X0P3ikpxEx/CsMw4OvHwK8LDLvV3eGKiIiIiMhxRlMof8XcdVk8tyiV7JJqvD0sOJwmj114Es7ycjJvuQXD05P4117F4ucHO5fBrm9gwtPgHeju0EVERERE5DijBO4I5q7L4v5PNlFtcwBQY3fiYTFYnV7AKW8+SV1WFt3e/TuesbFgmq7qW3A8DPmTmyMXEREREZHjkaZQHsFzi1Ibkrf97E6TXU8/R+WKFUQ99BB+Q4a4Xtj6OWSvg5FTwdPnEFcTERERERE5NkrgjiC7pPqgY6P3rmHiL18ROvn3hP5/e3ceHVWZ5nH8+xDSJKCIDbTGBE0cPGAg6bA5QjoMDSOrYHAcmzagqMeeAyqoc7Che5RoYxsWe4kQl6FxOW7DICIKduMItGB7gAAxLMEGkYbEIJsBiQGS8M4fVcQQsm83Vfw+5+TUrbfu8lS9t1L3ue973/uz232FpSWwehZ06gbx45o5ShERERERuVgogavGVR3CAbj81Almr8ugz9c5TM36X3ZdeR1XzJjx/YyfvQlH/g6D/wtC1CtVRERERESahhK4akwb1o3w0BDu2PUhPY9+ya83vso3Ye1p/UQaFhrqm6n4FKxNg6t6w/WjvQ1YRERERESCmpqLqpHcK5KQb45y9dJNtMIRVlrMkYdnMPpfYr+fKfNPcCIXkheAmXfBioiIiIhI0FMCV4O+H79DAQ4ACwkhbu8W4Gbfi6dOwLpn4NpBvj+RFqS4uJjc3FxOnTrldSgiInKRCAsLIyoqitBzPZVEpNEpgatG8aFDHH/nHSj1j0RZWsrxpUvpPHkSrTt3hk8XwHdHYcjj3gYqUonc3FwuvfRSoqOjfTeZFxERaULOOY4ePUpubi4xMTFehyMStHQNXDWOZDyHO3v2vDJ39iyHM56DwiPw6Xy4fgxE9vEoQpGqnTp1io4dOyp5ExGRZmFmdOzYUT0/RJqYErhqFGVlQXHx+YXFxRRt3errOln8nW/kSZEWSsmbiIg0J/3uiDS9WnWhNLPhwB+BEGChcy6twusDgT8A8cA459yScq/dBZzLcmY5515pjMCbw7XL3qn8hYID8GxvSLgDOndr3qBEREREROSiVWMLnJmFAAuAEUAs8HMzi60w235gIvBGhWV/CMwE/hm4AZhpZpc3PGyPrfXnr/8y3ds4RBrRsq15JKatJmb6ChLTVrNsa57XIUllshfD73tCagffY/ZiryOSOlqxdwVDlwwl/pV4hi4Zyoq9K7wOSeqh+NAh9o2fQMnhw42yvn379tGzZ89GWVdFa9eu5eabfQOwLV++nLS0tBqWqFp0dDRxcXEkJCTQt2/fxgpRROqgNl0obwD2OOf2OufOAG8Bt5SfwTm3zzmXDZytsOww4EPn3DHn3DfAh8DwRojbO4c/h8/egH73QYcuXkcj0iiWbc1jxtJt5BUU4YC8giJmLN3WpEncyJEjKSgooKCggIyMjLLy8gcaLcnEiROJiYkhISGBhIQEsrKymj+I7MXw3hQ4fgBwvsf3pjR5EhdodTV//ny6du2KmXHkyJGycuccU6ZMoWvXrsTHx7Nly5Zmj23F3hWk/i2V/MJ8HI78wnxS/5bapElcoNVfVd+1llB/5R3JeI6izZt918UHkDFjxjB9esNOQK9Zs4asrCwyMzMbKSoRqYvadKGMBA6Ue56Lr0WtNipbNrLiTGb2C+AXAFdffXUtV+2R1b+B0LaQ9IjXkYjU2hPv7WDnVyeqfH3r/gLOlJ5//qWouJRHl2Tz5sb9lS4Te1V7Zo7uUe+YVq5cCfjOOmdkZDB58uR6r6u+SkpKaN269oPxzp07l9tuu63pAvpgOhzcVvXruZug9PT5ZcVF8O4DsLmK3ulXxsGI+p9th8Crq8TERG6++WYGDRp0XvkHH3zA7t272b17Nxs2bGDSpEls2LChUeOcvXE2u47tqvL17MPZnDl75ryyU6WnePyTx1ny9yWVLtP9h9355Q2/rHdMgVZ/UPl3rTnqD+Dgb3/L6Zyq6xDAnTlDUXY2OEfBW29xOicHq2bY/DbXd+fKX/2qxm2XlJSQkpLCli1b6NGjB6+++irz5s3jvffeo6ioiAEDBvDCCy9gZqSnp/P888/TunVrYmNjeeuttygsLOTBBx9k+/btFBcXk5qayi23nHfOnZdffpnMzEzmz5/PxIkTad++PZmZmRw8eJA5c+aUfe5z585l8eLFnD59mrFjx/LEE0/U4tMTkebQIgYxcc696Jzr65zr27lzZ6/DqVreZsh5DwY8CO06eR2NSKOpmLzVVF4bc+fOJT09HYCHH36YwYMHA7B69WpSUlKIjo7myJEjTJ8+nS+++IKEhASmTZsGwMmTJ7ntttvo3r07KSkpOOeq3E50dDQzZ86kd+/exMXFsWuX78Dr2LFjJCcnEx8fz4033kh2djYAqampTJgwgcTERCZMmEBqaip33XUXSUlJXHPNNSxdupRHH32UuLg4hg8fTnHFgYy8VDF5q6m8loKtrnr16kV0dPQF23/33Xe58847MTNuvPFGCgoKyM/Pb9BnV1cVk7eaymsj2OqvKi2h/s4589VX5z/Pa5zeCp9//jmTJ08mJyeH9u3bk5GRwQMPPMCmTZvYvn07RUVFvP/++wCkpaWxdetWsrOzef755wF46qmnGDx4MBs3bmTNmjVMmzaNwsLCareZn5/P+vXref/998ta5latWsXu3bvZuHEjWVlZbN68mY8//hjwDVIydOhQ+vTpw4svvtgo71tE6qY2p8PygPJ9BaP8ZbWRBwyqsOzaWi7b8vzfE9C2I/S/3+tIROqkppayxLTV5BUUXVAe2SGc//mP/vXaZlJSEs888wxTpkwhMzOT06dPU1xczLp16xg4cCCffPIJ4DsI2b59e1k3qbVr17J161Z27NjBVVddRWJiIp988gk/+clPqtxWp06d2LJlCxkZGcybN4+FCxcyc+ZMevXqxbJly1i9ejV33nln2TZ27tzJ+vXrCQ8PJzU1lS+++II1a9awc+dO+vfvz9tvv82cOXMYO3YsK1asIDk5GYBf//rXPPnkkwwZMoS0tDTatGlTr8+mSjW1lP2+p7/7ZAWXdYG7698FLxjrqjJ5eXl06fL9z1lUVBR5eXlERETU+7OrqKaWsqFLhpJfeGHSEdEugpeGv1SvbQZj/VX2XWuO+gNqbCkrPnSIL24aCueSXec4e+IEkb97xneP2Abo0qULiYmJAIwfP5709HRiYmKYM2cO3333HceOHaNHjx6MHj2a+Ph4UlJSSE5OLvvcVq1axfLly5k3bx7gu53M/v2V96I4Jzk5mVatWhEbG8vXX39dtp5Vq1bRq1cvwJfo7969m4EDB7J+/XoiIyM5dOgQN910E927d2fgwIENet8iUje1aYHbBFxnZjFm9gNgHLC8luv/CzDUzC73D14y1F8WOMoPGPDlX+GfhkCbS72OSqRRTRvWjfDQkPPKwkNDmDas/qOs9unTh82bN3PixAnatGlD//79yczMZN26dSQlJVW77A033EBUVBStWrUiISGBffv2VTv/rbfeWrbNc/OuX7+eCRMmADB48GCOHj3KiRO+bqRjxowhPDy8bPkRI0YQGhpKXFwcpaWlDB/uu1Q3Li6ubH1PP/00u3btYtOmTRw7dozZs2fX9SNpuCGPQ2j4+WWh4b7yBgi2umrJpvaeSlhI2HllYSFhTO09td7rDLb6axHftWpUe4/YBqo4BL+ZMXnyZJYsWcK2bdu47777yu6xtmLFCu6//362bNlCv379KCkpwTnH22+/TVZWFllZWezfv5/rr7++2m2WPxF1rgXWOceMGTPK1rNnzx7uvfdeACIjfVfC/OhHP2Ls2LFs3Lixwe9bROqmxgTOOVcCPIAv8coBFjvndpjZk2Y2BsDM+plZLvDvwAtmtsO/7DHgN/iSwE3Ak/6ywFBxwADwdaHUqG8SZJJ7RfL0rXFEdgjH8LW8PX1rHMm9LrhktdZCQ0OJiYnh5ZdfZsCAASQlJbFmzRr27NlTpwOKkJAQSkpKajV/beYFaNeuXaXLt2rVitDQ0LKDqFatWpWtLyIiAjOjTZs23H333d4ctMTfDqPTfS1umO9xdLqvvAGCra6qEhkZyYED37dg5ubmlh2MNpdR144idUAqEe0iMIyIdhGkDkhl1LWj6r3OYKu/qr5rLaH+oIZ7xDbQ/v37+fTTTwF44403ylpDO3XqxMmTJ1myxHed5NmzZzlw4AA//elPmT17NsePH+fkyZMMGzaMZ599tiwR21rPmIYNG8aiRYs4efIk4Gu9PnToEIWFhXz77bcAFBYWsmrVqiYbOVNEqlarK4qdcyuBlRXKHi83vQlf98jKll0ELGpAjN756EnfAAHllRT5yht4wCTS0iT3imxQwlaZpKQk5s2bx6JFi4iLi+ORRx6hT58+551lvvTSS8sOCBp726+//jqPPfYYa9eupVOnTrRv377e68vPzyciIgLnHMuWLfPuoCX+9ib5/xNMdVWVMWPGMH/+fMaNG8eGDRu47LLLGr37XW2MunZUgxK2ygRT/VX1XWsp9VflPWIbQbdu3ViwYAH33HMPsbGxTJo0iW+++YaePXty5ZVX0q9fPwBKS0sZP348x48fLxuds0OHDjz22GM89NBDxMfHc/bsWWJiYsqumauLoUOHkpOTQ//+vi70l1xyCa+99honT55k7NixgG/AlTvuuKOsFVVEmk/th4S6GB3PrVu5iJwnKSmJp556iv79+9OuXTvCwsIu6NLVsWNHEhMT6dmzJyNGjGDUqMY5sE1NTeWee+4hPj6etm3b8sorVYzSWEspKSkcPnwY5xwJCQllgwYEi2Cqq/T0dObMmcPBgweJj49n5MiRLFy4kJEjR7Jy5Uq6du1K27Zteeml+l1z1hIFU/1V9V0L5voD3yAx5waGKW/WrFnMmjXrgvL169dfUBYeHs4LL7xwQfmgQYPKRmWdOHEiEydOBHwjUpZ3rsUNYOrUqUydemHX3s8++6y6tyEizcCqG3HKC3379nUt5r4i1Q0Y8PD25o9HpA5ycnJq7D4lIiLS2PT7I9JwZrbZOde3stdaxG0EWqwmGjBARERERESkPtSFsjrnrjP56Elft8nLonzJm65/E2l2Y8eO5csvvzyvbPbs2QwbNsyjiKQqqqvApvoTEWnZ1IVSJEjl5OTQvXv3C4alFhERaSrOOXbt2qUulCINpC6UIhehsLAwjh49Sks7SSMiIsHJOcfRo0cJCwureWYRqTd1oRQJUlFRUeTm5nL48GGvQxERkYtEWFgYUVGV3llKRBqJEjiRIHXu5r4iIiIiEjzUhVJERERERCRAKIETEREREREJEErgREREREREAkSLu42AmR0G/uF1HJXoBBzxOggJatrHpClp/5KmpP1LmpL2L2lKLXX/usY517myF1pcAtdSmVlmVfdiEGkM2sekKWn/kqak/UuakvYvaUqBuH+pC6WIiIiIiEiAUAInIiIiIiISIJTA1d6LXgcgQU/7mDQl7V/SlLR/SVPS/iVNKeD2L10DJyIiIiIiEiDUAiciIiIiIhIglMCJiIiIiIgECCVwtWBmw83sczPbY2bTvY5HgoeZdTGzNWa208x2mNlUr2OS4GNmIWa21cze9zoWCT5m1sHMlpjZLjPLMbP+XsckwcPMHvb/Pm43szfNLMzrmCRwmdkiMztkZtvLlf3QzD40s93+x8u9jLE2lMDVwMxCgAXACCAW+LmZxXoblQSREuA/nXOxwI3A/dq/pAlMBXK8DkKC1h+BPzvnugM/RvuaNBIziwSmAH2dcz2BEGCct1FJgHsZGF6hbDrwkXPuOuAj//MWTQlczW4A9jjn9jrnzgBvAbd4HJMECedcvnNui3/6W3wHPpHeRiXBxMyigFHAQq9jkeBjZpcBA4E/ATjnzjjnCryNSoJMayDczFoDbYGvPI5HAphz7mPgWIXiW4BX/NOvAMnNGlQ9KIGrWSRwoNzzXHSALU3AzKKBXsAGbyORIPMH4FHgrNeBSFCKAQ4DL/m76S40s3ZeByXBwTmXB8wD9gP5wHHn3Cpvo5IgdIVzLt8/fRC4wstgakMJnEgLYGaXAG8DDznnTngdjwQHM7sZOOSc2+x1LBK0WgO9geecc72AQgKg+5EEBv+1SLfgO1FwFdDOzMZ7G5UEM+e7v1qLv8eaEria5QFdyj2P8peJNAozC8WXvL3unFvqdTwSVBKBMWa2D1/378Fm9pq3IUmQyQVynXPneg4swZfQiTSGfwW+dM4dds4VA0uBAR7HJMHnazOLAPA/HvI4nhopgavZJuA6M4sxsx/gu3h2uccxSZAwM8N37UiOc+53XscjwcU5N8M5F+Wci8b3v2u1c05nr6XROOcOAgfMrJu/aAiw08OQJLjsB240s7b+38shaJAcaXzLgbv803cB73oYS6209jqAls45V2JmDwB/wTf60SLn3A6Pw5LgkQhMALaZWZa/7FfOuZUexiQiUhcPAq/7T3LuBe72OB4JEs65DWa2BNiCb9TmrcCL3kYlgczM3gQGAZ3MLBeYCaQBi83sXuAfwO3eRVg75uvqKSIiIiIiIi2dulCKiIiIiIgECCVwIiIiIiIiAUIJnIiIiIiISIBQAiciIiIiIhIglMCJiIiIiIgECCVwIiIStMys1Myyyv1Nb8R1R5vZ9sZan4iISG3oPnAiIhLMipxzCV4HISIi0ljUAiciIhcdM9tnZnPMbJuZbTSzrv7yaDNbbWbZZvaRmV3tL7/CzN4xs8/8fwP8qwoxs/82sx1mtsrMwj17UyIiclFQAiciIsEsvEIXyp+Ve+24cy4OmA/8wV/2LPCKcy4eeB1I95enA391zv0Y6A3s8JdfByxwzvUACoB/a+L3IyIiFzlzznkdg4iISJMws5POuUsqKd8HDHbO7TWzUOCgc66jmR0BIpxzxf7yfOdcJzM7DEQ5506XW0c08KFz7jr/818Coc65WU3/zkRE5GKlFjgREblYuSqm6+J0uelSdG25iIg0MSVwIiJysfpZucdP/dN/A8b5p1OAdf7pj4BJAGYWYmaXNVeQIiIi5elMoYiIBLNwM8sq9/zPzrlztxK43Myy8bWi/dxf9iDwkplNAw4Dd/vLpwIvmtm9+FraJgH5TR69iIhIBboGTkRELjr+a+D6OueOeB2LiIhIXagLpYiIiIiISIBQC5yIiIiIiEiAUAuciIiIiIhIgFACJyIiIiIiEiCUwImIiIiIiAQIJXAiIiIiIiIBQgmciIiIiIhIgPh/qkiBMhrW4AsAAAAASUVORK5CYII=\n","text/plain":["<Figure size 1080x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"tags":["pdf-inline"],"id":"NoO6VenBTzCG"},"source":["## Inline Question 2:\n","Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n","\n","## Answer:\n","[FILL THIS IN]\n"]},{"cell_type":"markdown","metadata":{"id":"r4h4R4NSTzCG"},"source":["# Layer Normalization\n","Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n","\n","Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n","\n","[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"]},{"cell_type":"markdown","metadata":{"tags":["pdf-inline"],"id":"g4ZEpa7lTzCH"},"source":["## Inline Question 3:\n","Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n","\n","1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n","2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n","3. Subtracting the mean image of the dataset from each image in the dataset.\n","4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n","\n","## Answer:\n","[FILL THIS IN]\n"]},{"cell_type":"markdown","metadata":{"id":"Zx9J5mR9TzCH"},"source":["# Layer Normalization: Implementation\n","\n","Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n","\n","Here's what you need to do:\n","\n","* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_forward`. \n","\n","Run the cell below to check your results.\n","* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n","\n","Run the second cell below to check your results.\n","* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n","\n","Run the third cell below to run the batch size experiment on layer normalization."]},{"cell_type":"code","metadata":{"id":"_St_6juUTzCH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619613198110,"user_tz":-480,"elapsed":1713,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"8857ec46-96ea-41a8-dd0e-b66aaf02c00e"},"source":["# Check the training-time forward pass by checking means and variances\n","# of features both before and after layer normalization.\n","\n","# Simulate the forward pass for a two-layer network.\n","np.random.seed(231)\n","N, D1, D2, D3 =4, 50, 60, 3\n","X = np.random.randn(N, D1)\n","W1 = np.random.randn(D1, D2)\n","W2 = np.random.randn(D2, D3)\n","a = np.maximum(0, X.dot(W1)).dot(W2)\n","\n","print('Before layer normalization:')\n","print_mean_std(a,axis=1)\n","\n","gamma = np.ones(D3)\n","beta = np.zeros(D3)\n","\n","# Means should be close to zero and stds close to one.\n","print('After layer normalization (gamma=1, beta=0)')\n","a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n","print_mean_std(a_norm,axis=1)\n","\n","gamma = np.asarray([3.0,3.0,3.0])\n","beta = np.asarray([5.0,5.0,5.0])\n","\n","# Now means should be close to beta and stds close to gamma.\n","print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n","a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n","print_mean_std(a_norm,axis=1)"],"execution_count":49,"outputs":[{"output_type":"stream","text":["Before layer normalization:\n","  means: [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n","  stds:  [10.07429373 28.39478981 35.28360729  4.01831507]\n","\n","After layer normalization (gamma=1, beta=0)\n","  means: [ 4.81096644e-16 -7.40148683e-17  2.22044605e-16 -5.92118946e-16]\n","  stds:  [0.99999995 0.99999999 1.         0.99999969]\n","\n","After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n","  means: [5. 5. 5. 5.]\n","  stds:  [2.99999985 2.99999998 2.99999999 2.99999907]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1woZgSwGTzCH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619615813065,"user_tz":-480,"elapsed":1109,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"076ccf59-6029-45f5-b852-80825ae17b4e"},"source":["# Gradient check batchnorm backward pass.\n","np.random.seed(231)\n","N, D = 4, 5\n","x = 5 * np.random.randn(N, D) + 12\n","gamma = np.random.randn(D)\n","beta = np.random.randn(D)\n","dout = np.random.randn(N, D)\n","\n","ln_param = {}\n","fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n","fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n","fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n","\n","dx_num = eval_numerical_gradient_array(fx, x, dout)\n","da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n","db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n","\n","_, cache = layernorm_forward(x, gamma, beta, ln_param)\n","dx, dgamma, dbeta = layernorm_backward(dout, cache)\n","\n","# You should expect to see relative errors between 1e-12 and 1e-8.\n","print('dx error: ', rel_error(dx_num, dx))\n","print('dgamma error: ', rel_error(da_num, dgamma))\n","print('dbeta error: ', rel_error(db_num, dbeta))"],"execution_count":56,"outputs":[{"output_type":"stream","text":["dx error:  1.433615657860454e-09\n","dgamma error:  4.519489546032799e-12\n","dbeta error:  2.276445013433725e-12\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"eMLif7cVTzCI"},"source":["# Layer Normalization and Batch Size\n","\n","We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"]},{"cell_type":"code","metadata":{"id":"A_qRHxbqTzCI","colab":{"base_uri":"https://localhost:8080/","height":689},"executionInfo":{"status":"ok","timestamp":1619618708286,"user_tz":-480,"elapsed":116568,"user":{"displayName":"Mingyu Yang","photoUrl":"","userId":"04100620278597188865"}},"outputId":"fb5de62e-46b2-4161-aefa-6d47bbf723e4"},"source":["ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n","\n","plt.subplot(2, 1, 1)\n","plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n","                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n","plt.subplot(2, 1, 2)\n","plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n","                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n","\n","plt.gcf().set_size_inches(15, 10)\n","plt.show()"],"execution_count":64,"outputs":[{"output_type":"stream","text":["No normalization: batch size =  5\n","Normalization: batch size =  5\n","Normalization: batch size =  10\n","Normalization: batch size =  50\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA3AAAAJcCAYAAAC480YuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXycV33v8c+Z0UiakTSjzVpHq3fH+xLbsZ09cWwRoLSkCSU0kJKyNaaE3MsaDG0pXFqK2wK3QGkJtEAupSFUtrNBFi/xFjmO491aLI21WPs20mzn/vE8mhlttixZGsn+vV8vvTQzzzPznBkpr+jr3zm/o7TWCCGEEEIIIYSY/iyxHoAQQgghhBBCiLGRACeEEEIIIYQQM4QEOCGEEEIIIYSYISTACSGEEEIIIcQMIQFOCCGEEEIIIWYICXBCCCGEEEIIMUNIgBNCiBhTSu1SSv3ptT5XjE4ptVcptSLW45jJlFLVSqm7zdtfUEr9aBKu8X+VUl++Bq9zv1Lql9diTEIIEWtK9oETQoirp5TqjrrrAPqBoHn/z7XW/zH1oxJjoZS6H/ik1vo+8/52YI7W+oMxHdgQSikNHAeWaa1D5mN/Dbi11o/EcmzmWKqBP9Nav3SNXu8R8/U2XovXG+H1jwMf0Fofm4zXF0KIqSIVOCGEGAetdfLAF3ABuD/qsXB4U0rFxW6UM8cUf04fA346hde7rCu89zzgwUm+xo3i58BjsR6EEEJMlAQ4IYS4hpRStyul6pRS/1sp1QD8m1IqTSn1P0qpS0qpNvO2O+o5ryil/sy8/YhSao9S6u/Mc6uUUlvGeW6JUuo1pVSXUuolpdR3lVI/G2XcVxpjulLq35RSF83jz0Yde49S6qhSqlMpdV4pNVDZCk+xM+9vH7i+UqpYKaWVUo8qpS4AvzMf/39KqQalVIc59puinm9XSv29UqrGPL7HfKxcKfUXQ97PMaXUH4zwPuOBO4FXr/SzNM//nPmeupRSJwZeUykVr5RqVUotiTo3SynVq5SaZd5/l/m5tCul9imllkadW23+jhwDei4TsP4P8NXRjiul3q2Uese8xitKqYWXucYc8zP/sFKq1vw5fkwptcb8vNqVUv8c9fzZSqnfKaValFLNSqn/UEqljjKO6J/tPyuluqO+Asqocl7u81wI/F9gvfmcdvPxf1dG1XHgOh9VSp0zP/vnlFJ5Uce0+X7Omu/lu0opFTXMV4CyUT5nIYSYMSTACSHEtZcDpANFGP/ibwH+zbxfCHiBfx712bAWOA1kYvwB/69D/hAd67n/CRwEMoDtwMOXueaVxvhTjKmiNwFZwD8AKKVuBp4GngRSgVuB6stcZ6jbgIXAZvP+LmCueY03geipqH8HrAJuwfh8/xcQAn4ChKc/KqWWAflA+QjXmwuEtNZ1YxzfeWAT4AK+CvxMKZWrtfYBv4i+LvAQ8LLW+pIy1tf9GPhzjM//X4DnlFIJQ84vA1K11oFRrv9roBN4ZOgBpdQ8jKrSp4FZwE7gt2ZIHXYNYOAaazE+hz8GvgN8Ebgb42f7gFLqtoFLAH+LUQVcCBRg/B5dltb6U1HV6Y1AG/Ab8/Bon+dJjMrofvO5w4KiUupOczwPALlADcbPINq7gDXAUvO8zVHHTgLFSinnld6DEEJMZxLghBDi2gsBX9Fa92utvVrrFq31f2mte7XWXcDfYASX0dRorX+otQ5ihJNcIPtqzlVKFWL8IfuU1tqntd4DPDfaBS83RqVULrAF+JjWuk1r7ddaD1SwHgV+rLV+UWsd0lp7tNanxvYxAbBda92jtfaa4/ix1rpLa92PERaWKaVcSikL8BFgm3mNoNZ6n3nec8A8pdRc8zUfBn5phqyhUoGusQ5Oa/3/tNYXzff2S+AscLN5+CfAQ1GB+WEiUzMfA/5Fa33AHOtPMNZJrot6+X/UWtcOvPfRhgB8GfjykGAGRgArNz97P0bAtWME3Mtd46+01n1a6xeAHuDnWusmrbUHeB1YYb73c+Zr92utLwHf5vK/t4OYlchngb/QWleYr3m5z/NK/gTjd+1N8+f+eYyKXXHUOd/QWrdrrS8AvweWRx0b+LmPWEUUQoiZQgKcEEJce5e01n0Dd5RSDqXUv5hT/zqB14BUpZR1lOc3DNzQWveaN5Ov8tw8oDXqMYDa0QZ8hTEWmK/VNsJTCzCqKuMVHpNSyqqU+oY5xa6TSCUv0/xKHOla5mf9S+CDZtB7iNHXuLUBKWMdnFLqQ1HTINuBxeZY0FofAHqB25VSC4A5REJyEfDEwPPM5xZg/FyGvffL0VrvBOowqnnR8jCqUAPnhczXzL/CNRqjbntHuJ8MoJTKVkr9QinlMX8eP8N871eilLIBvwL+U2v9i6jHR/08x2Do++0GWhj8fhuibvcy+L+bgZ97+xivJ4QQ05IEOCGEuPaGtvd9ApgPrNVaOzGmGYIxRW2y1APpSilH1GMFlzn/cmOsNV9rpMpFLTB7lNfswZh2OSBnhHOiP6sPAO/BmM7nAoqjxtAM9F3mWj/BqNDcBfRqrfePct45QCml8kc5HqaUKgJ+CHwKyDCn9R1n8M9tYPrmw8CvooJ7LfA3WuvUqC+H1vrnUc+9mjbQXwS+wODP8yJGUBwYr8L4GXvGeY2hvm4+f4n5O/FBxv47+08YUz+/FDW+K32eVxrr0PebhDE91TPqMwZbCFRrrTvHeL4QQkxLEuCEEGLypWBUNtqVUunAVyb7glrrGuAwsF0ZDTfWA/ePZ4xa63qMtWnfU0azE5tSaiDg/SvwYaXUXUopi1Iq36xGARwFHjTPXw380RWGnYIxzbAFI6h8PWoMIYw1Zd9WSuWZ1br1A2vKzMAWAv6ey3SYNKdVvsTwqYAWpVRi1FcCkIQRKi4BKKU+jFExivYz4A8wws3TUY//EPiYUmqtMiQppcqUUmOu/g0Z9ysYYSd6D8BngDLzs7dhhPB+YN94rjGCFKAb6DAD75NjeZJS6s8xPt8/Gdj+wHSlz7MRcI8wVXTAzzF+15abP5+vAwe01tVjfD+3YfweCyHEjCYBTgghJt93MNYmNQNvALun6Lp/AqzHCER/jTHNsH+Uc680xocBP3AKaMJonIHW+iDwYYymJh0Y3R0HqiRfxqiYtWE0rPjPK4z3aYwpch7ghDmOaJ8F3gYOAa3ANxn8/7GngSUYoepy/oXhDV0ewgiwA1/ntdYnMALhfoxwsQTYG/0krXUtRrMVjbF+bODxw8BHMRrBtGFU/h65wriu5EsYzVsGrnEaIzj+E8bP7X6M7SxGWvs3Hl8FVmL8XMsxGqqMxUNAKXBRRTpRfmEMn+fvgHeABqVU89AXNfeb+zLwXxgV5tlc3RYLD2H87IUQYkaTjbyFEOIGoZT6JXBKaz3pFcBYUEp9CHhsLBtBK6X2Ap8aaK4xwev+GLiotf7SFU8WMaGMzdsf1lo/EOuxCCHEREmAE0KI65RSag1GpaoKuBejI+D6axFaphtzrd/vgO9prZ++0vnX8LrFGFNFV2itq6bqukIIIW5cMoVSCCGuXzkYmxd3A/8IfPw6DW+bMdZVNXLlaZrX8rp/hbEu7VsS3oQQQkwVqcAJIYQQQgghxAwhFTghhBBCCCGEmCHiYj2AoTIzM3VxcXGshyGEEEIIIYQQMXHkyJFmrfWskY5NuwBXXFzM4cOHYz0MIYQQQgghhIgJpVTNaMdkCqUQQgghhBBCzBAS4IQQQgghhBBihpAAJ4QQQgghhBAzhAQ4IYQQQgghhJghJMAJIYQQQgghxAwhAU4IIYQQQgghZggJcEIIIYQQQogbSnllOff+6l6W/mQp9/7qXsory2M9pDGbdvvACSGEEEIIIcRkKa8sZ/u+7fQF+wCo76ln+77tAJSVlsVwZGMjAU4IIYQQQghxXQmGgvhCPnxBH/3BfvoD/cb3UD/fOvStcHgb0BfsY8ebOyTACSGEEEIIISZfeWU5O97cQUNPAzlJOWxbuS2mYURrTUAHwsEpHKSG3B7pWF+wL/zYWI4Nfb3+YD+BUOCqx9zQ0zAJn8S1JwFOCCGEEEKIGWy0KYEhHeKeonuGhZ3oEDTWY6OFrejqli/ooz8UeTykQxN6XzaLjQRrQvgr3ho/6LYz3jnsWPQ5CXEjHLMksH3/dlr7WoddLycpZ0LjnSoS4IQQQgghhJjBvvPmd0acEviFPV/gC3u+MO7XVSgS4xLDwSc6CA08nhKfMjw4jRC2rvZYvDUei5qcfou9gd5BgRcg0ZrItpXbJuV615oEOCGEEEIIIWYYf8jPgfoD7Kraddmpf9tWbiPRmjhiaIq3xo96LMGaQJwlDqXUFL6rqTEwtXQ6TTm9GhLghBBCCCGEmAFCOsSRxiPsrtrNizUv0tbfRootBXucHW/AO+z83KRc/mzJn8VgpNNfWWnZjAlsQ0mAE0IIIYQQYprSWnO8+Ti7qnfxfNXzNHmbsMfZud19O1tKtrAhfwMv1rw4o6cEiqsjAU4IIYQQQohp5mzbWXZV7WJX1S7quuuIs8SxMX8jny35LLe5b8Nhc4TPnelTAsXVUVrrWI9hkNWrV+vDhw/HehhCCCGEEEJMqdrOWnZVG6HtXPs5LMrC2py1bCnZwp2Fd+JKcMV6iGKKKKWOaK1Xj3RMKnBCCCGEEELESGNPI7urd7O7ajfHW44DsDJrJV9Y+wXuKbqHTHtmjEcophsJcEIIIYQQQkyh1r5WXqp5iV1VuzjSeASNZmH6Qp5Y9QSbizeTm5wb6yGKaUwCnBBCCCGEEJOs29fNyxdeZlf1Lt64+AZBHaTEVcLHl3+cLcVbKHYVx3qIYoaQACeEEEIIIcQk8Aa8vFb3GrurdvNa3Wv4Qj7yk/N55KZH2FKyhXlp867LfdbE5JpQgFNK3QfsAKzAj7TW3xhyvBD4CZBqnvM5rfXOiVxTCCGEEEKI6cof9LPv4j52Ve/i9xd+T2+gl0x7Ju+f/362lGxhaeZSCW1iQsYd4JRSVuC7wD1AHXBIKfWc1vpE1GlfAp7RWn9fKbUI2AkUT2C8QgghhBBCTCvBUJDDjYfZVbWLF2tepNPXiTPeyZaSLWwp2cLq7NVYLdZYD1NcJyZSgbsZOKe1rgRQSv0CeA8QHeA04DRvu4CLE7ieEEIIIYQQ04LWmrcuvcXu6t08X/08zd5mHHEO7iy8ky0lW1ifux6b1RbrYYrr0EQCXD5QG3W/Dlg75JztwAtKqb8AkoC7R3ohpdRjwGMAhYWFExiSEEIIIYQQk0NrzZm2M+ys2snuqt1c7LlIvCWeW923cl/JfdzqvhV7nD3WwxRjcewZePlr0FEHLjfc9RQsfSDWoxqTyW5i8hDw71rrv1dKrQd+qpRarLUORZ+ktf4B8AMwNvKe5DEJIYQQQggxZtUd1eENtqs6qrAqK+vz1vPJFZ/kjoI7SIlPifUQxdU49gz89nHwe437HbXGfZgRIW4iAc4DFETdd5uPRXsUuA9Aa71fKZUIZAJNE7iuEEIIIYQQk6q+u57d1bvZVbWLk60nUShWZa/igws/yD1F95CWmBbrIYrx0BpefCoS3gb4vUZF7joPcIeAuUqpEozg9iDwgSHnXADuAv5dKbUQSAQuTeCaQgghhBBCTIpmbzMv1rzIrqpdVDRVALAkcwlPrn6SzcWbyU7KjvEIxVULBaHxHbjwBlzYb3zvqh/53I66qR3bOI07wGmtA0qpTwHPY2wR8GOt9TtKqa8Bh7XWzwFPAD9USv0lRkOTR7TWMkVSCCGEEEJMC52+Tl6ueZldVbs40HCAkA4xJ3UOj694nPuK76PAWXDlFxHTh68XPEciga3uEPR3Gsec+VB0C5x7Cfrahz/X5Z7asY7ThNbAmXu67Rzy2FNRt08AGyZyDSGEEEIIIa6lXn8vr9a9ys6qnez17MUf8lOQUsCjix9lS8kW5qbNjfUQxVj1NA+urtUfhVAAUJC1CJa8HwrXQ+E6SDXD+NA1cAA2u9HIZAaY7CYmQgghhBBCxJwv6GOPZw+7q3bzSt0reANeshxZPLTgIbaUbOGmjJtkg+3pTmtorRwc2FrOGsesCZC/Cm553AhsBWvAPso6xYF1btKFUgghhBBCiOkjEApwsOEgu6p28XLNy3T5u0hNSOX+0vvZUrKFldkrsShLrIcpRhMMQMOxwYGtx+yFmJhqBLUVHzS+5y2HuISxv/bSB2ZMYBtKApwQQgghhLhuhHSIo01H2VW1ixdqXqC1r5VkW3J4g+21uWuxWWSD7Wmpv9tYsxZev3YY/D3GsdQimH2nMRWycD1kzgPLjRm+JcAJIYQQQogZTWvNydaT7Kraxe7q3TT0NJBgTeA2921sLdnKRvdGEqxXUZ0RU6OrEWrfgJr9RmBreBt0EJQFsheb1bV1xpczL9ajnTYkwAkhhBBCiBmpsr2SXdW72F21m+rOauIscWzI28C2ldu4o+AOkmxJsR6iGKA1tJyLTIW8sN9YzwYQZwf3atj0hBHW3Gsg0Rnb8U5jEuCEEEIIIcS0U15Zzo43d9DQ00BOUg7bVm6jrLQMT7fHqLRV7eZ022ksysKanDU8ctMj3F10N64EV6yHLgACPqh/ywhqtQeM770txjFHphHUVj9qTIfMXQpWmdY6Vmq6bcu2evVqffjw4VgPQwghhBBCxEh5ZTnb922nL9gXfizOEkduUi61XbUALJu1jC0lW9hcvJlMe2ashioG9HVA7aFIhc1zGALmzy+9NNLKv/AWyJgN0vHzspRSR7TWq0c6JhU4IYQQQggxbfT4e/i7w383KLyB0VGyvqeeT6/8NPeV3Ed+cn6MRigA6PAY69cumGvYGo8DGpTVqKit/ogR2ArWQUp2rEd7XZEAJ4QQQgghplQgFOBi90WqO6up7qg2vndWU9NRQ5O3adTnBUNBHl3y6BSOVAAQCkHz6cHr19ovGMdsScaea7d/zghs+ashITm2473OSYATQgghhBDXnNaa1r5WajprhgW12q5aAqFA+FxXgotiZzHr8tZR4irh6Xeepq2/bdhr5iTlTOVbuHEF+uFiRVRgewP62o1jydlGUFv3CeN79hKwSqSYSvJpCyGEEEKIcesL9FHTWTMoqNV01lDVWUWXryt8ns1iozClkFJXKXcU3EGxs5gSVwlFziLSEtMGvWZuUu6wNXCJ1kS2rdw2Ze/rhuJtg9qDUevX3oRgv3Escx4sendkDVtaiaxfizEJcEIIIYQQ4rJCOkRDT8OgKtpAUKvvqUcTaYqX7cim2FnM1pKtFDuLKXIWUewqJi8pD6vFOqbrlZWWAYzYhVJMkNbQURuZCnnhDWg6YRyzxEHeClj7mBHYCtZCkjSImW6kC6UQQgghhACgo79jxCmPFzov0D9QkQGSbEmDwlmJ06ikFTmLcNgcMXwHN7Bjz8DLX4OOOnC54a6nYOkDEAoaAS06sHV6jOckOKHgZrM75HrIWwnx8vObDqQLpRBCCCGEAMAf9FPbVTusklbdWU1rX2v4PKuy4k5xU+wsZn3ueopdxRQ7ja9MeyZqsqfRjRZIxHDHnoHfPg5+r3G/oxae/QTs+Y5xu7/TeDwlD4rWR6ZDZi2CMVZFxfQhAU4IIYQQ4jqjteaS99KwKY/VndV4uj2EdCh8bkZiBkXOovC6tIGqmjvFjc0So82VRwokv33cuH21IU5rowoVCkR9Db0f/Zh/lHPG8BrB0V7zKl5jzOOMut9VD1E/U8B4H82nYeWHIoHNVSDr164DMoVSCCGEEGKG6vH3GNWzEdam9QZ6w+clWhPDUxyjK2lFriKc8c4YvoMRhILw7UXQ3TD8mNUGsxZcOQhFBykdnPr3MJQlDiw287vV/B53dfetl3l+xc9GubCC7e1T+lbFtSFTKIUQQgghZqiR9kwbCG3Re6YpFHnJeRQ7i1mZvdIIa2ZQy07KxqIsMXwXl9FZD54j4DkMdYeN9vW+7pHPDfqNKtIVQ8/lgtFYg9QIj1mvdI0RHlOWya96Vb5qVCmHcrkn97oiJiTACSGEEEJMgfLK8lG7KmqtaetvG3HK49A905zxTopdkT3TBoJaobOQBGtCrN7e2Ph64OLRSFjzHIk01LDYIGcxLHsIjv8XeFuHP99VAA/9fGrHPBPc9dTgKacANrvxuLjuSIATQgghhJhk5ZXlg/Y1q++p50t7vsQzp58hEAqMumdaibMkvDZtYOrj0D3Tpq1QEJrPmEHtMNQdMbohDkxpTC0y1mXlrwb3ashZCrZE41jBzRJIrsbAukBp+nJDkDVwQgghhBCTqNnbzHt/8146+juGHbMoC2uy11DsKh405TEveex7pk0bXQ1RYe2wUWkbCKWJLshfZYS1/FVGYLvS/mLShVLcwGQNnBBCCCHEFAmGghxvOc7rda/zuud1TrScGPVcrTU/2vyjKRzdNeLrhfqjg6trnXXGMUscZC+GZX8cqa6lzwbLVa7BW/qABDYxaZ6t8PCt509zsd1LXqqdJzfP570r8mM9rDGZUIBTSt0H7ACswI+01t8YcvwfgDvMuw4gS2udOpFrCiGEEEJMNx39Hez17OV1z+vs9eylrb8Ni7KwfNZytq3cxn+c/A+avc3DnpeTlBOD0V6lUMhoR+85EglsjdFTIQuNKY/uTxiBLXepMd1RiGnq2QoPn//123j9xu+wp93L53/9NsCMCHHjDnBKKSvwXeAeoA44pJR6Tmsd/mcmrfVfRp3/F8CKCYxVCCGEEGJa0Fpzuu00r9e9zmt1r3Gs+RghHSItIY2N+RvZ5N7ELXm34EpwAZCblDtoDRwYrf23rdwWq7cwuq7GqCYjh8FTEZkKmeCC/JWw6TOR6ZDJs2I7XiHGIBAMcbG9j5rWHrY/9044vA3w+oN86/nT13eAA24GzmmtKwGUUr8A3gOMNk/gIeArE7ieEEIIIUTM9Ph7eOPiG7zmeY09dXvCLfxvyriJx5Y+xqb8TdyUcdOIa9cGuk2O1oUyZny9UP/W4K6QA+3oLXGQfZMxjdG92ghsGXOufiqkEFOkuz9ATUsPta291LT0UtPaG77tafcSDF2+98fFdu9lj08XEwlw+UD0hhN1wNqRTlRKFQElwO9GOf4Y8BhAYWHhBIYkhBBCCHFtaK2p6qwy1rLVvc6RpiMEQgFSbCmsz1vPJvcmNuZvJNN+hWYcprLSstgGtlDI6AoZXV2LngrpKjSC2tqPGd9zl8lUSDGthEKapq5+alp6uNDaG/6qaTG+t/b4Bp2f6rBRlO5gqdvF/ctyKUx3UJiexKd/WUFjZ/+w189LnRm/71PVxORB4Fda6+BIB7XWPwB+AEYXyikakxBCCCHEIH2BPg42HAw3IPF0G3uUzUmdw4cWfYhN+ZtYlrUMm8UW45GOQXfTkK6QFdDfaRxLcBpTITf+pVldWwXJWbEdrxBAnz9IXVsklNW0mFU0s5rWHwiFz7UoI3QVZTjYfFM2helJFKY7KMpwUJDuwGUf+b/Tz29ZOGgNHIDdZuXJzfMn/f1dCxMJcB6gIOq+23xsJA8Cn5zAtYQQQgghJkVdVx2ve4wq28GGg/QH+7HH2Vmbs5aPLP4Im/I3kZucG+thXp6vFxqODe4K2XHBOKasxlTIJX8U6QqZMVemQoqY0FrT1uuPVNHMqY4Dtxs6+wad74i3UpjuYPasJO6YP4vCDDOkpTvIS7UTH3f1v8cD69xmahfKce8Dp5SKA84Ad2EEt0PAB7TW7ww5bwGwGyjRY7iY7AMnhBBCiMnkD/p5s+nNcJWtsqMSgMKUQm5138qm/E2syllFgjUhxiMdRSgELWcHV9ca34maClkQ2Wst35wKGe+I7ZjFDSW6YchAMIuupnX1Bwadn5WSEK6aFaUnRW5nOMhIikcpFaN3EjuTsg+c1jqglPoU8DzGNgI/1lq/o5T6GnBYa/2ceeqDwC/GEt6EEEIIISZDU28Tezx7eL3udfbX76fH34PNYmNNzhreP+/9bHJvoshZFOthjqz70vCukAObgsenmFMhPx3ZKDslO7bjFTeEgYYh4XB2mYYh8VYL7nQ7RekO1hSnUZiRRFG6g8IMBwVpDuzxM2zT+hgbdwVuskgFTgghhBATFQwFebv5bV6re409nj2cbD0JQLYjO1xlW5u7FodtCitTx56Bl78GHXXgcsNdTw3fqNrvNbpChsPaEWiPngq5KDINMn81ZM6TqZBiUozUMGRgXdpIDUPSHDajSUhGEoXpdorSk8JVtBxnIhbLjVdFm4hJqcAJIYQQQkwnbX1t7L24l9fqXmPfxX109HdgVVaWZy3n0ys/zSb3Juamzo3NdKxjz8BvHzcCGhit+n/7OHTVQ9KsqK6Q70DInF7mKjCqa2s+anaFXC5TIcWonq3wXPWarvE3DMkJNwspTL98wxBx7UmAE0IIIcSMFNIhTrWe4rW613jd8zpvX3objSY9MZ3b3Ldxq/tW1uetxxnvjPVQjcqbf8geU34vvPiUcTs+BfJXwC2PR7pCpuRM/TjFjPRshWdQV0VPu5fP//pttNbcNj9rXA1D7lyQZa5JM0Jafpodm1WqvdOBBDghhBBCzBhdvi72X9zP657X2ePZQ7O3GYViceZiPr7849yafysLMxZiUTH8QzPQD5dOQcNxaHjb+OqoHf38T7xhToWUdUBibHyBEO1eHx29ftq9fr72PycGtcQH8PqDfOaZtxi6WCrbmUBhuoMNczIjVTSzknajNgyZaSTACSGEEGLa0lpzvv280ebf8zoVjRUEdICU+BQ25m1kk3sTt+TdQoY9IzYD7G01AlpjVFi7dCoyDdLmMFr4xyeBr2f4810FkLVwascspo2hQay9109b78B9H229/vDt9l6/+eWjxzfi1srDaODL71pEkRnU3NIw5LogAU4IIYQQ04o34OVg/cHw3mwXey4CMD9tPo8sfoRN+ZtYOmspcZYp/DMmFIL26sFVtYa3obMuck5yDuQsgbn3Qs5iyFkK6aVGZW3oGjgAm91oZCJmPF8gRIfXCFcjBbFw+LqKIGa1KFLtNlIdNlId8eQ4E1mQ4zTu222kJsWHj3/mmbe41NU/7DXyU+08urFkMt+6iAEJcEIIIYSIudrOWl7zGGvZDtUfwhfyYY+zs5hSxLcAACAASURBVD53PR9d+lE25m8kJ2mK1oT5+6DpxOCqWsNx8HUZx5XFmPJYtN4IbNmLje/JWaO/5kC3ySt1oRQxNRDEOszq10DQGha+xhHEXA4baWYQm5+TQpojflBAM4KZ+d1hIzkhbszTGb+4deGgNXAAdpuVJzfPn/BnIqYf2UZACCGEEFPOF/RxpPFIuMpW3VkNQLGzmE3uTcZm2tmriLfGT+5AepoHV9Qa3obmM5FNseOTIwEtx/yetcionolJNZ6uigNGC2IdXqMy1h6eshgJYh1eP91DNpiOFh3EUu1GGHOZoSvNDF0uh3l7nEFsIibyeYnp53LbCEiAE0IIIcSUaOhpCG+m/Ub9G/QGeom3xLMmdw2b8jdxa/6tFDgLJufioRC0VkLjkLDWVR85x5lvBrWoqlpaieyzFgNDuyoCxMdZeHhdEQtyUoYFsY7eyP2JBLFUh400M4gNHDOCmY2UKQpiQoDsAyeEEEKIGAiEAhy7dCzc5v9M2xkAcpNyuX/2/dzqvpU1OWuwx13japav15gCGR3UGt8Bv9lERFlh1gIouS1SWcteAkkxaoQiBvG0e9n+23eGdVX0BUL8656q8H2LImrqoY1sc2riSEEs1REJasnxcbKptJjRJMAJIYQQYlzKK8vZ8eYOGnoayEnKYdvKbazPWx+usu29uJcuXxdxKo4V2St4YtUTbHJvotRVeu0qGV2Nw6tqLedAmxsQJziNkLby4UhlbdYCsCVem+uLCWvt8bH/fAt7zzez71wz1S29o56rgFefvIPUJAli4sYlAU4IIYQQV628spzt+7bTFzQ2Aq7vqefzr38ebe46lWnP5O7Cu9nk3sS63HWkxKdM7IKhoBHMhq5X62mKnOMqNELaTe+LVNZSi0CmvU0rvb4AB6ta2Xe+hb3nmjlR34nWkJwQx9qSdB5eX8y/vHqephG6Kual2inMcMRg1EJMHxLghBBCCDEmHf0dnG07y9n2s3znyHfC4W2ARpNiS+FHm3/EgvQF499Mu7/bnAJ5LGoK5AkImC34LTbIWgBz74las3YT2NMm+A7FZPAHQ7xV286ec83sO9dCRW0b/qAm3mphRWEqn7l7HrfMyWSp24XNavzOZCTFS1dFIUYhAU4IIYQQg/hDfmo6ajjTdoYzbWc4236WM21naOhpuOJzu/3dLMpYNLYLaW00EWk4PjistVaCWckjMdUIaKs/EglrmfMgbpK7U4pxC4U0pxq62He+mb3nmjlY1UqPL4hSsDjPxUc2lrBhdiZritNH3VR6oHuidFUUYjgJcEIIIcQNSmtNS18LZ1rPDApr59vP4w/5AYizxFHiKmFV9irmpc1jXto85qbO5eFdD1PfUz/sNUfdqy0YgJazZkiLCmu9LZFz0oqNgLbswUhYc+bLFMgZ4EJLL3vONbP3fDP7z7fQ2uMDoDQzifetdLNhTgbrSjNIdYw9eL93Rb4ENiFGIAFOCCGEuAH0Bfo4334+EtTajKpaW39b+JwsRxZz0+ayPm99OKyVOEuwWW3DXm/bym1s3/Nl+rQ//FiisrFt5Tbo6zS6PkaHtaaTEDTXNFkTIGshzN8KOUsjUyATnZP+OYhr41JXP/vOG1Mi955vpq7NmN6a7Uzg9nmzuGVOJhvmZJDrkv3yxPTlb2rC85kncP/Dt4mbNSvWwxkzCXBCCCHEdSSkQ1zsvjgsqF3oukDI7Mxoj7MzJ3UOdxbeydy0ueGqWmpi6pivU9bdA80t7HA6aIizkhMIsq29lbJfPQ69zZETHRlGQFv7WCSsZcwFq/wJMpN09fk5WNUaXsd2urELAGdiHOtKM3js1lJumZ3J7FlJsleamDGav/d9vEeOcOl73yf3K0/FejhjJht5CyGEEDNUl68rHNAGwtrZ9rP0DOx3BhSkFISrafPS5jE3bS7uZDdWy8hrj0alNbRVQf1bxtcb34PA8C6BxNnh1s9GwlpKjkyBnIH6A0HerGkPr2N7q66DYEiTEGdhTXE6t8zJYMPsTBbnu7BKK38xTYT6+gh2dBLq7CDY0UGws3Pw/Y5O87F2gs0t9J08CVqj4uOZ8/JL06oKJxt5CyGEEDNYIBSgprNmUFg703Zm0Bq0lPgU5qXN492z3x0Oa3NS5+CwjaPleigIzWcjYa3+LWMqZH+ncdxig5B/5OcG+owAJ2aUYEjzzsUO9p5rYd/5Zg5Vt9LnD2FRsNSdysduK2XD7ExWFqWRaLvK8C/EVdA+nxGyOjsJdnQQMr8b4csIYqGOyPFgZ+S+7h/hH5UGKIXF6cRqfgVaIutvtQ7NqCqcBDghhBBiGmn2Ng+a+ni2zWgq4gsZTSHiVBzFrmKWZy3ngbQHwmEt25E9vqlrAR9cOjkkrB2PtOyPsxv7qS19wKiq5S4z1q/90yroqB3+ei73BN69mCpaayqbe9h3rpm951rYX9lCh9cI5fOyk3lwTSEb5mSytjQdZ+LwNZBCXI4OBgl2dhKKqoKFw9fA/SHha6BipntH38gdwJKUhMXlxOpKxep0klBSijXVZYYzF1aXE6sr6n6qC6vTiSUlBWUxtqnwNzVx/p57jZkFAP4AHb/+NbM+8fFpVYUbjQQ4IYQQIgb6g/3hpiLRlbXWvtbwObPss5iXNo91C9eF16qVuEqIt46zhb6v12guUn80EtaaTkaqafEpkLsUVn/YCGq5y0Zfr3bXU/Dbx8HvjTxmsxuPi2mpoaOPvWanyH3nWmjoNPbxy0+1c++ibDbOzWT97AyyUhJjPFIxXteyKYcOhQh1d0cqYdFhrKPDnJY4QiWso4NQd/dlX1slJmJ1mcHK5cTmdpO4aJFRHRstjLlcWFNSULaJ/4NC8/e+jw6Fhr3fmVKFkwAnhBBCTCKtNfU99YOmPp5tO0tNZw1BbWxSnGhNZHbqbG5z3zZorVpa4gQ2pu7rMLo/hitrx6D5NJiNTLCnGwFt/ScjYS2tBCxj3Hx76QMcqm6j4M1vkaWbaVKZ1C55kjVLHxj/mMU11dHrZ39lS3gd2/lLxtrINIeNW2ZnhtexFWU4pPHIdWJoUw6tNaGeXiNshcNX+/Aq2KD7ZuWsqwuGhJxoymbD4nKFg5htVhaWOXPClbFh4Ssc2FxY4mO7j6P36FHwD5kG7vfjraiIzYCukjQxEUIIIa6Rbl83Z9vPDmss0u2P/Gu0O9kdrqYNfBWkFFx9U5FoPS2RqlrDMeN7a2XkeEpuJKQNfE1wf7VnKzx8/tdv4/UHw4/ZbVb+9n1LZO+uGOnzBzlU3Rpex3bc00FIGz+Xm0vS2TAngw1zMlmY48QijUdmvFBfH/6LF/HX1eGrq6P/7Fnaf/mMEbqUwuJyGZWwQGD0F7Faw2vCosOY1WXed7qGhLFIVUwlJkrwn0ST1sREKXUfsAOwAj/SWn9jhHMeALYDGnhLa/2BiVxTCCGEmCzlleXseHMHDT0N5CTlsG3lNspKy4adFwgFuNB1YdhaNU+3J3xOii2FuWlzeVfpuyKt+tPmkmRLGv8AtYau+khFbaC61lkXOSe1yAhoy/8EcpcbUyKTs8Z/zRGHofnGrpODwhuA1x/ka/9zgrxUO6kOm/Fljyc+boxVPXFVAsEQxzwd4XVsRy604QuEiLMoVhSm8hd3zmXDnEyWF6TKz2AG0j4f/vp6fHV1+D0e/HUe83sdvosegpeaBz9BqciaLiAuI4OU979/cBgzw1e4EpYk2z7MROOuwCmlrMAZ4B6gDjgEPKS1PhF1zlzgGeBOrXWbUipLa910udeVCpwQQohYKK8sZ/u+7fQF+8KPJVoT+eyaz1LkLOJMq1lRazeaivSbm1JblZViZ7FRTUs39lOblzaPnKScif1hpDW0VUcqagNfPZfMExRkzo1U1HKWGmHNPoFplyPo6Q9wprGLUw1dnKrv5KT5vbPvMv+qP4Qj3kqaIx6X3Qh1aY54XA4bqeb9VEe8eTueNIfNPCbBbyitNWebutlztpl955s5UNlKV7/xc1iY62TDbKPCdnNJOkkJskpmutOBAP6GxnAo83s8+D11+MywFmhsHBTIsFqx5eZic7ux5ecR73Zjy8/H5najEhOpeegDg7owqoQE5rz04oxoyiGGm6wK3M3AOa11pXmRXwDvAU5EnfNR4Lta6zaAK4U3IYQQIlZ2vLljUHgD6Av28ddv/HX4fqY9k7mpc3lw/oPMS58XbiqSYE2Y2MVDQWg5b4a0qKmQfR3GcUsczFoIczcbIS13GWQvhoTkiV03SjCkudDay6n6TiOsNRjfa1oiHeGS4q0syHVy/7I8/udYfbhrYbRZKQl8+4FltPf6aff6ae/xGd97/bT3GrdPNXTS4fXT1usnGBr9H5KT4q2kmsEvLckIdS6HjTRH5Haq3UZakhEAr8fgV9fWy75zLUbjkfMtXOoy/kAvTHfwrmV5bJiTwfrSDDKSJ/g7KK45HQoRuHQpHM6GVdLq6yEYVcVWiricHGz5eSStXRsOZzZ3PvH5+cRlZ6PiRv7TvX77V2d0Uw5xdSYS4PKB6P7BdcDaIefMA1BK7cWYZrlda7176AsppR4DHgMoLCycwJCEEEKIqxMMBTnUeGjQnmpD/fDeHzI3dS4Z9oxrcEE/XDo1pG3/2+A3g5I1wWjbf9P7ItW1rEVgu3adAdt6fJGQVt/FqcYuzjR0hadEWhQUZyaxOM/FH610Mz8nhYW5TvJT7eG1U2uK00dcA/fFrQvZNHds/+Kvtaa7P2CGOz/tXl8k6JkBsK3XR4d5+2RHZ/j2WIJf9DTO6NsuswqYGq4CGiFxOgS/1h4f+8+3sOecUWUbCNCZyQncMjuDDXMyuGV2JgXp49jfT1xTWmuCLS1R4exipJJWV4f/4kX0kEYZcbNmYcvPx758Oc6ysnA4s7nd2HJyUONs7jHTm3KIqzPZ9fU4YC5wO+AGXlNKLdFat0efpLX+AfADMKZQTvKYhBBC3OC01pxoOUF5VTm7q3ZzyXsJhUIz/H9BuUm5rMtdN74L+b3QeGJI2/4TEDT2dCM+GXKWwMoPRcJa5jywXpt9t3yBEJXN3Zyq7+LkQFhr6KSxMzLNKj0pngU5KTx0cyELclNYmONkbnbyFTdrHmhU8q3nT3Ox3Uteqp0nN8+/qgYmSilSEm2kJNooSB/7+9Ja09UfMMKcGfzaev10mMGvzXyso9cIgPXjDX7hqZ3RITDenAZqw2W/uuD3bIVn2Od1z6JsDla3htexnag3NktPTohjXWk6f7q+mA1zMpmXnSxrlaaY1ppQRwe+qLVnfo8Hn6cuXEXTfYOr9ta0NGxuNwkLF5Jyz92RKlp+Pra8PCyJk7NFQ+mz/z0pryump4kEOA9QEHXfbT4WrQ44oLX2A1VKqTMYge7QBK4rhBBCjEtNZw07K3eys2on1Z3V2Cw2NuVvoqy0jB5/D18/8PVha+C2rdw2thfv7xretv/SKTC3CiAx1Qhoaz9mhrXlkF469rb9l6G1prGzPzztcWAa5PlL3fiDRmCJt1qYk5XMhtmZLMhNYUGOkwW5KcxKThh3MHjvivyYdJxUSuFMtOG8yuAXCmm6fYFwsAtP8xy4HTXNs90MfgOPXSb3kZwQN+L6voEqn8tu41RDFz99owZfwJjm5mn38plnjqK10eUt3mphZVEqT9wzj1vmZLLM7SLOGvuK4PUu2N09OJzV1Q1qFhLq6Rl0vsXpxJafT0JpCcmbNpkBLR9bvlFJsyRNoEmREGM0kQB3CJirlCrBCG4PAkM7TD4LPAT8m1IqE2NKZSVCCCHEFGn2NrO7ajflleUcbzmOQrEmZw2P3PQIdxfdjSvBFT433vMmOyr/mwYL5IRgW9F9I3ahpLd1yBTIY8YatoEKXlIW5C2HBVsjDUZSCyfUtj98aV+AM43dw9aqtfdGpk/luRJZkOvkzgVZ4emPJZlJ2G7wQGCxRAe/sU9BHAh+7T2RaZ5tvT46zLV90dM823p9XGz3hkPg5YJfSBvh7/sfXMnqonTs8RPYSkKMKOT1ms1Bhoczv8dDsKNj0PnK4QhPaXSsWRMJZ2YVzep0xuidCBEx7gCntQ4opT4FPI+xvu3HWut3lFJfAw5rrZ8zj92rlDoBBIEntdYt12LgQgghxGi6fd28dOEldlbu5EDDAUI6xML0hXx29WfZXLyZnKSc4U869gxle39Imd8beaz+h+AoNMJXdGWt40LkHFeh0Vhk6YORBiMpI7z+VQqFNLVtvZysj6xVO93YRXVLT7gxnSPeyvycFLYszmVhbgrzs43KmstxbaZgCkN08Cvk6oLfwFTP2771+xEm6BpdPse6ZvBG5G9qwvOZJ3D/w7dH7KYY8vkIXLxoTHMcoZNjsGXwn50qIcGomuXnk7hsaaSTY77RLMSamipTVcW0Jxt5CyGEuC74gj5e97zOzsqdvFr3Kv3BftzJbraWbqWspIzS1NKRn6g19LXDd9dBd8PlL5IxJ6plv7lmzXEVc/hG0dHrj0x/bOjkZH0XZxq76PUZ0y+VguKMJBbkRKY+Lsxx4k6zy4bMM8SGb/wOT7t32OP5qXb2fu7OGIxoZqjfvp32Xz5D8h13kHLXncM6OQaamga32rfZsOXlGlW0fHfUGjSj7b41M1MCmpgRLreNgAQ4IYQQM1ZIhzjSeITyynJeqHmBLl8X6YnpbC7eTFnRZpYmZqF6mqCrEbqjv5qgq8H43t0Iwf7LX+jDu4y2/YkTmz7lD4aovNQzbK1afUdk3V2qw8bCHKc59dEIbHOzk3HEy75eM9mzFZ4Ru3b+7fuWxGQd4XQV7O6h7+1j9FZU0HPgIN4DBwafYLFgy8mJNAaJnuLodhM3axbKKlNRxcw3WfvACSGEEFNLa7S3nVMX32BnzfPsbDxIk78Lh4rjLmsqW0liXXMncdU/BO83R34Ne7oxxTE5y6ioJWdBcja8/m3wjjDL31UARbdc5TA1l7r6w5ten27o4mRDF+eausJNRWxWxexZyawrzWBBTkp4rVpWyvibiojp61p07bzeaK3x19XhrajAe/QovRVH6T99GkIhUAqL02k0+QmFwGrFWbaVvL/5G5RNpgiLG5tU4IQQQsRe0G9Ww6KqYkOqZrU9jeykm52OBCrjbcRpzcZeL2U9vdzWH8SelA0p2UYYC39lRcJacg4kzYK4UfZZOvYM/PZxo/X/AJsd7v9HWPrAqEP3+oKcbeoa1Kr/dGMXrT2+8Dk5zsRw58eFuUZYK81Mnhb7jgkxVUL9/fS9c8IMbBX0Vhwl2NwMgCUpCfuyZdhXrMC+YgW2/Dyq3vsH6P5IdVwlJDDnpRdHXAsnxPVGKnBCCCGmntbQ1xEJZANf4amLUWGtd+T+Vi1JGTzvSqM8QXEsFSCZVfZcvpy5knvdt5KaNtsIaomuiXd4XPoAh6rbKHjzW2TpZppUJrVLnmSNGd5CIU1dm3fQWrVT9V1URTUVsduszMtJ4d5F2cZ6tVwnC3JSSHWMb3NeIWaywKVL9FZU4K04ireigr533glvbG0rLCR5wy3hwJYwZ86gqY/127+KDoUGvZ4Ohbj0ve+T+5WnpvR9CDHdSIATQghxdYJ+6Lk0PIh1NUTWlw08Fugb/nxrvFENS84y9kErXDeoatZjd/K7zvOU1+/jjYYDBLWPeWnz+MvSMrYUbyE3OXdS3tazFR4+f6gIr39H+DHbAcXqxv30+0OcbuiiJ6qpSFG6gwU5Tu5flhdeq1aY7pCmIuKGpAMB+s+eHRTY/HV1AKj4eBIXLybtQw/jWLEC+/LlxGVmXvb1vEePgt8/+EG/H29FxWS9BSFmDJlCKYQQ17Njz8DLX4OOOnC54a6nRp4OqDX0d44exKLD2ijVMuxpkWAWnraYPfyxxNRh1TJ/0M/ei3spryznldpX6Av2kZeUx9bSrWwt2crctLmT8OEMtv5vXx7UTGSAUnBzcToLzWra/JwU5mWnkJQg/wYqblzBzk68b72Ft6KC3ooK+t46Rqi3F4C4WbOwr1yJfcVyHCtWkLBwIZZ4qUILcTVkCqUQQtyIhq7p6qiF33wCTpZDcuaQdWZNEBje4tyolpnVsbRiKFwbWVuWnGPcTsk215YlXNXwQjrEm41vsrNqJy/UvEBHfwepCam8Z857KCstY/ms5VPSzKOquYef7q8ZMbwBoOGXf75+0schxHSltcZXXR2urHmPVtB/9pxx0GIhccECXH/wB9hXrMCxYjlxeXnSiEeISSQBTgghrkfdTbDrfw1uyAHG9MeTz5rVMjOYFawdofmHGcxGqJZN1OnW05RXlbOrahcNPQ3Y4+zcUXAHZaVlrM9bj80y+R3mgiHNK6ebeHp/Da+euYTNqrDbrINavA/IS7VP+niEmE5CXi99x4/TOxDYKioItrcDYHE6sa9YjrOsDPvyFdiXLMaSlBTjEQtxY5EAJ4QQ14sOD5z8LZx8Dmr2AaNNkVfwv6uncGDg6fawq2oX5ZXlnGs/h1VZuSXvFj698tPcUXAHDptjSsbR3uvjmcO1/PSNGmpbvWQ7E/jMPfN48OYC9p1rGXGfric3z5+SsQkRK/6GhvBUSG/FUfpOnoRAAID40lKS77rTWLu2YgXxJSUoi3RPFSKWJMAJIcRM1lZjBLYTv4G6Q8ZjWYvg9s/B4X8z1qwN5XJPzdD62nih+gXKq8qpaDIaD6zIWsEX136Re4vvJT0xfUrGAXDc08FP99fw7FEP/YEQN5ek87n7FnLvTdnYrMYfo7JPl7gRaL+fvlOnBu29FqivB0AlJmJfupSMRx/FvmI59mXLiEtLi/GIhRBDSYATQoiZpvkcnPyNEdrq3zIey10Gd34ZFr0HMs2GH+mlI+9rdtfkteDu9ffy+9rfs7NqJ/s8+wjoAHNS57Bt5TbuK74Pd8rUhEcAXyDEruP1PL2/hiM1bdhtVv5wlZsPrS9iQY5zxOe8d0W+BDZxXQm0tUXWrlVU4D1+HN1nrPeMy8s1Kmsf+Qj2FStInD9PNskWYgaQACeEENOd1nDplBHYTjwHTe8Yj7vXwD1/BQvvh/SS4c8b6DY5li6UE+AP+dl/cT/lleX8vvb3eANecpJyePimhykrKWNe2rwpbWjQ0NHHfx68wH8euEBzdz/FGQ6+/K5F/NEqNy67/HEqrl86FMJ3/vygVv6+6mrjYFwciYsWkfbHDxh7ry1fji0nJ6bjFUKMjwQ4IYSYjrSGhmOR0NZyFlBQuB7u+yYsfNfYpkIufeCaBzZjeJqjl45SXlnOC9Uv0NbfhjPeSVlpGWUlZazMXolFTd06Ga01B6taeXp/DbvfaSCkNXfOz+Lh9UXcOneW7M0mrkvB7h763j4WCWxHjxLq6gLAmpaGfcUKXH/4PhwrVpC4eDGWxMQYj1gIcS1IgBNCiOlCa/AcgRPPGqGtvQaUFYo3wrqPw4J3GZ0hY+hc27lwB0lPt4dEayK3F9zO1pKtbMzfiM06tRWuXl+AZysu8vT+ak41dOGy23h0YwkfXFtEYcbUNEYRYiporfF7POGpkL0VR+k/fRpCIVCKhDlzcG7ZEm7lbysqklb+QlynJMAJIUQshYJQe8AIbCefg04PWGxQejvc+lmYXwZJGTEdYn13PbuqjQ6SZ9rOYFVW1uWu45PLP8mdhXeSZJv6FuLVzT389I0anjlcS1dfgEW5Tr75h0t497J87PHWKR+PENdayOej7513wlMhe49WELzUDIDF4cC+fBkpH/uYMR1y2VKszpHXdQohrj8S4IQQYqoFA1Czxwxtv4WeJrAmwJy7jTVq8+4De2pMh9jR38Hz1c+zs2onRxqPALB01lI+d/Pn2Fy8mUx75pSPKRjSvHqmiZ/sM/Zui7Moti7J5UPri1hVlCbVBjEj+Jua8HzmCdz/8G3iZs0KPx64dIneo0fDga3v+HG03w+AraCApPXrw638E+bORVnlHyqEuFFJgBNCiKkQ8EHVa8b0yFPl4G0FmwPm3guL3m18T0iJ6RC9AS+v1r5KeWU5ey7uIRAKUOIq4VPLP8XWkq0UOAtiMq6Bvdt+9sYFLrT2kpWSwF/ePY+Hbi4gyylresTM0vy97+M9coSGv/k6jpvX4DVDm7+2FgBls5G4eDFpDz+MfcVyHMuXDwp6QgihtB5to9fYWL16tT58+HCshyGEEBPn74PzvzMakZzeBf0dEJ8C8+8z2v3PvgviY7tOKxAKcKD+AOWV5bx84WV6A71k2bPYUrKFstIyFqQviFlla6S92z60vojNN+WE924TYroK+Xz4PR78nov46+rwezz0nz9H9+9fMda7mqyzMnEsNypr9hXLSbzpJizx8bEbuBBiWlBKHdFarx7pmFTghBDiWvL1wNkXjdB29gXwdUNiqtE1cuG7YfYdEJcwZcMpryxnx5s7aOhpICcph20rt7G1ZCvHmo+xs3Inu6t309rXSkp8CltKtrC1ZCursldhtcRmepYvEGL3Ow08va+aw+bebe9baezdtjBX1viI6UP7/fgbG8PhzFdXh7/OY4S2ujoCTU2Dn2CzoRKi/tu3WnGWbSXvm9+U6b9CiKsiFTghhJiovk4487yxufbZlyDgBUdmJLSV3ApT3J0RjPC2fd92+oJ94cfiVBwp8Sm09bcRb4nntoLbKCstY1P+JuKtsftX/8bOPv7jwAV+fvACl7qMvdseXl8se7eJmNHBIIGmphHDmd/jwd/YCMFg5AkWC7acHGz5+djcbvN7PvHmfZTi/Ob70P394aeohATmvPSiTJEUQgwjFTghhLjWeluNaZEnnzOmSQZ9kJwDKx82QlvRLRCjKtaAHW/uGBTeAAI6QI+/h7/a8FfcVXgXKfGxW3enteZQdRs/2V/N88cbCGrNHfOz+JDs3SamgNaaYHPz4HDmGaimefDX14PZRGRAXFYWNrcb+6pVON35xIeDmhtbdjbKNvo/NtRv/yo6FBo8hlCIS9/7PrlfeWpS3qMQ4vo0oQCnbi8KlAAAIABJREFUlLoP2AFYgR9prb8x5PgjwLcAj/nQP2utfzSRawohRMz0NMOp/zGmR1a9BqEAuApgzUeNNW3uNWCJ3dqsYCjI6bbTHGo4xMGGg9T31I94nj/k571z3jvFo4sYunebMzGOD28o5oPriijKmPotCcT1SWtNsL09UjGrq8Pn8USFNc+gahiANSMDmzsf++KbcG7eHFVNy8OWl4clYfzTn71Hjw4LhPj9eCsqxv2aQogb07gDnFLKCnwXuAeoAw4ppZ7TWp8YcuovtdafmsAYhRAidjrrI6GtZi/oEKSXwvpPGd0j81ZCjNavhHSIc+3njMBWf5DDjYfp9HUCUOQswhHnoDfQO+x5OUk5Uz1UYPjebQtl7zYxQcHOzsgUx+hwZt4P9Q7+/be6XNjy80mYM4fk227D5s7Hlm9W0vLysDgmr6lQ6bP/PWmvLYS4sUykAnczcE5rXQmglPoF8B5gaIATQoiZpf2CsT/bid9A7UFAw6wFsOmzRmjLXhyT0Ka1pqqjioMNBznYcJDDDYdp628DID85n7sK72JNzhpuzrmZ7KTsEdfAJVoT2bZy25SNORTSvHKmiaf31/DKaWPvti1LcvlT2btNjEGop8eomg2qnEUqaaHOzkHnW5KS/j979x0fVZX3cfxzZzKTzCQhPQTSKKFDSCJJAAUrKItdVBRBdF123Udx1XXXsg9iWRfFvoq7PhZ0RRAQBcS2VkSlh95CT0IgPSGZmcxk5jx/3DBJSKjp4fd+vXwlc++Ze891QPPNOed39BGz2Fisw4Z615+ZovWgZgxs3a06hBCiKTQmwEUDWbVeZwPpDbS7QdO0kcAu4H6lVNbxDTRNmwJMAYiLi2tEl4QQ4iwV7tHXs21bAofW68c6D4KLH9NDW0SfFu+SUoqso1newLbm8BoK7AWAPoo2ImaEN7B1Deha7/1je4wFqFeF8tjx5lRic7JgbTb/WXlA9m4TJ+SprNTL7Oc0MM0xOxt3cXGd9pqfn7c4iDUpCVN0jDegmWOiMQQFyS8FhBAd3llXodQ0bRxwhVLqrurXE4H02tMlNU0LA8qVUpWapv0euFkpdcnJritVKIUQLSZ/pz7Ktm0JHNmsH+uaoq9n63cVhPVs8S7llOewOne1dx3bEdsRAMIt4aRFpXn/iQmMaZM/qG49VLN3m8PlIa1bKJOGy95tHZkrL4+cBx4k5qUX61VTVC4Xrtzc+tMcq7+vys+v014zmTB17VqriqO+/uxYsRBjWFib/HMvhBBNrbmqUOYAsbVex1BTrAQApVRhrZdvAc814n5CCNE4SsGRLXpg27YYCnbqx2OHwuXP6KEtuGVnARyuOOwNa2sOryGnXP/PaKhfKEM6DyEtKo3ULql079S9zf7gevzebX4mA9cly95t54q8l17Gvm4dOX9+COuQITX7ouXkUHXkCNSuvGg0YurSBVN0NP4jR9SsP6sOaz4REWitWAhICCHag8YEuDVAL03TuqMHt/HArbUbaJrWRSl1rAza1cD2RtxPCCHOnFL6lMhjoa14H2gGiD8f0n4Hfa+ETl1arDsF9gJvYFudu5qDRw8C0MncidSoVCb2n0haVBoJwQltNrAdc6TMwYerDvJh9d5t8WFW/ja2HzeeF0uQVfZu66g8Nhu2deuo+HUl5T8tx5m5GwDbqlXYVq3CJyoKU0w0/mmp+hTH6nBmjonGp3NnNB/ZwUgIIRrjrP8rqpSq0jTtHuAr9G0E3lFKbdU07UlgrVJqCTBV07SrgSqgCJjcBH0WQoiT83gge7Ue2rYvgdIsMPjoG2pf8CfoMxYCWmbj3GJHcZ0Rtr2lewEIMAVwXufzuKnPTaRFpdEntA8Gre2PPDS0d9tFvSOYNLwbF8rebR2Scrmwb95MxS+/UrHyV+wbN4HLhWYyYQgO1rfO8HjAx4fgG66nyxNPtHaXhRCiQzvrNXDNRdbACSFOatN8+PZJKM2GoBi4dBok3gTuKjj4S3VoWwrlh8Fohp6X6Btr9xkD1tBm715pZSlrj6z1hrbM4kwALD4WUjqneNew9Q3ti4+h/YxE2JxVLN5wiPd+qdm77ebUWNm7rQNSHg+Vu3ZR8etKPbCtWauX49c0/Pr3x3/YUKxDh2GOi2XvVVfX2UtN8/Ul4Zv/1lsLJ4QQ4sw01xo4IYRoWZvmw9Kp4LLrr0uzYPE9sO59yN8OtgLwsUCvy6DfNdD7cvBr3jVY5c5y1uetZ3WuXilyR9EOFAo/ox9JkUmMSR5DalQqA8IHYDK0v2mF+wsq+KB677ay6r3bZlw/iGuSZO+2jsSZleUdYbOtWo27qAgAc7dudLrmavyHDsM/PQ1jcLD3PbnTn0DVXt+GHv7yZ71Bl8entWj/hRDiXCIBTgjRfnz7RE14O8ZdCQdWwMDr9ZG2XqPA3HwjQjaXjYy8DO+UyG2F23ArNyaDicERg7l78N2kdUljUPggzEZzs/WjOXk8ih935fPer/v5cVc+Rk32butoqgoKqFi5Sg9sv67ElaMXz/GJjCRgxAVYhw7Df9hQTFEn3vTdvmEDuFx1D7pc2DMymrPrQghxzpMplEKItqeyHAozIX+XXimyYFfN9w3SYHpJs3TFUeVgY/5Gb2DbnL+ZKlWFj+bDoIhB3n3YBkcMxs+nfe9vVmpzsWBdFv9ZeYADhTYiAn2ZkB7HrWlxsndbO+cuL8e2Zg0Vv+qBrTJTn9prCAzEmp6mj7ANG4q5Rw8J6EII0QbIFEohRNujFFQU6KEsfycUZFZ/vwvKsmvaaUYI7QHhveHoIag8Wv9aQTFN1i2n28mm/E3eNWwb8zfi8rgwakb6h/Xn9gG3kxaVRlJkElaTtcnu25q2HSrj/V/3e/duS+0Wwp9H9+HyAVGYfdp+YRVRn8fpxJ6xwTvCZt+8GdxuNF9frOel0Omqq/AfNhS//v3RjDIVVggh2hMJcEKI5uVxQ8nB6lG06tG0Y987ao2amfwhvBfED4eI3hDeRw9toT3Ap3oq4vFr4ABMFr2QyVlyeVxsLdiql/U/vJqNeRtxuB1oaPQN7cutfW8lrUsaKZEpBJgDzvo+bY2zysNXWw/z/q/7WbP/2N5t0Uwc2o3+XWXvtvZGud04tu/AtvJXKn75Fdv69SiHAwwG/AYNJOyuu/AfNhRLcjIGX9/W7q4QQohGkAAnhGgaLgcU7q6e8phZE9YKd0OVo6adf4QezgZcBxHVIS28N3SK1suRn0ziTfrXhqpQnqYqTxU7inZ4A9v6I+uxV+mBsHdIb8b1HkdqVCrndT6PIN+gM/230OZ8mpHDzK92cqjETtdgC1NGdqeowuXduy0uVPZua4+UUjj37feOsFWsXo2ntBQA314JBN94o14tMjUVY2BgK/dWCCFEU5I1cEKIM2MvbnhtWvEB4Nh/TzQIia8JZ7WDWguU8q/NozzsLNrpXcO27sg6yl3lAPQI6kFqVCrpXdIZ0nkIIX4hLdq35vZpRg6PLNqM3eWud+7iPrJ3W3vjOpKnj7D9upKKlSupOnwYAJ+uXbxr2Kzp6ZgiI1u5p0IIIRpL1sAJIc6MUlCWU39tWsFOqMivaWf01ac9dk2GxPHVUx97Q1iCPrWxVbqu2F2yWx9hy13N2iNrKXOWARDfKZ4rul9BWlQaqVGphFvCW6WPzanM4WJ3Xjm788p5cum2BsNb506+vHtHWiv0TpwJd2kpFatX6yNsK1fi3KtvAm8MDsY6dCj+Q4fqlSLj4qTwiBBCnEMkwAlxLnO7oGhv/bVpBZngqqhp5xekT3vsfbn+NaKPHtyC48HQsgUQlu1dxivrX+FwxWGi/KOYmjyV/uH9WZOrFx1Ze2QtRQ59D6vogGgujbuU1KhUUqNSifI/cUn09kQpxZGyyuqgdpQ9+RX69/nl5B+tPOX788pO3Ua0PI/DgX39en2E7ddfcWzbBh4PmsWCdcgQgm+4Af9hQ/Ht2xftVNONhRBCdFgS4IQ4F1QerTXdsVZQK94Hnqqadp1i9GCWMrHu9Ef/CGgDv+FfumcpT/z6BJVuPYDkVuTyyIpHvOc7Wztzftfz9dL+XdKIDohura42CZfbw8Eim3dEbU9+OXvyytmTX0F5Zc3nFujnQ0JkABf1jqBnZAAJEQEkRAZw61srOVTiqHfdrsGtMzoq6lJVVTi2bKFi5Uoqfl2JPSMD5XSCjw+WxETC775bLzySmIhmbp97CgohhGh6EuCE6CiUgvK8+mvTCjL16ZDHGHz0yo4RfaD/1dXVHnvpYc23ZassepSH0spSih3FFDoKKXYUU+Qo8n4tchRRXFlc53VDgsxBfDj2Q2IDY9vlVLKKyir25lewO/+oHtTyKtidX86Bwgpc7pp1ylGd/EiIDGDceTH0jPDXw1pkABEBvg0+918u71tvDZzFZOShy/u0yHOJupRSOHfv9q5hs61ejadcX4/p27cvIbfeqge284ZgDGi+zeiFEEK0bxLghGhvPG4o3l9rJK1WQRFHaU07c4AezLqNqFmbFt4HQruDsXmqDdYOZN4AdlwYq32spLIEj/I0eK1O5k6E+oUS4hdCfKd4kiKTWLhrYYNty5xlxHWKa5ZnaipKKQornHVG0/SwVs6h0ppRMqNBIz7MSkJEAKP7d6Zn9Whaz8gAAnzP7D/Z1ybrI5C1q1A+dHkf73HR/Fw5Od4RtopVK3HnFwBgio2l05gx+A8fhjU9HZ/Qli3uI4QQov2SACdEa9s0v+Gy+C57dQGR49amFe4Gd601TAGd9XA2cFzN2rTwPtCpa6OnPXqUh7LKMm8A84avyiKK7PVHx04WyALNgYT5hdUJZCG+IYRZwgjxDSHEL4RQv1BC/UIJ9gvGZKgfMn/O+Zncitx6x9vS2ja3R5FTbK83mrY7r5xSu8vbzmo20jMigPQeYfSM8CehejQtLtS/STfPvjY5WgJbC6oqLsa2alX1KNuvuA4cBMAYFuYtOmIdOgxzjHwmQgghzo5sIyBEa2poY2rNAH4hYC/CW5ZfM0BItwbK8vcCy+mXvj8+kBVXFlNkL6KosqjB6YullaW4Vf0qhlA3kIX4hhBqCdW/Voew0wlkZ2rZ3mVM/2U6DnfNiJWf0Y/pw6cztsfYRl//TDhcbvYVVNQbUdtXUEFlVU2IDQ8w14yiVX9NiAwgqpOflO/vADwVFdjWrfNOi6zcvh0Ag78/1tRUfYRt6FB8e/Vql9N7hRBCtA7ZRkCItqhwDyx7kGVmjVc6d+Wwj5GoKjf3FZcw1mmDix7RA1pEHwjtCSa/epfwKA9ljpJ6I2LH1pMdP5WxpLLkpIHsWOCKC4xjcMTgBsPYscBmaqZpmCdzLKTVrkJ5X8p9zRreSmxObzjbXV1AZHdeOVnFNo79/kvTIDbESkJkACN6hXtDWs+IAIKtUnyiI1EuF/ZNm7wjbPaNm8DlQjOZsCQnE3HfVKxDh2IZNAjNR/4XK4QQounJCJwQLclWBFsXwcaPIHs1y/ytTA8PxVGrJLivx8P/FJeSOGFx/TVkjroB7XQC2bFRseNDWFsIZG2FUopDpQ72VIe03d5qj+UUlDu97cw+BnqE+9cbTese7o+fqWW3UxDNx5WXR84DDxLz0osYw8Ko3LnTG9hsa9ehbDbQNPz699enRA4bhjUlBYNFqnsKIYRoGicbgZMAJ0Rzq3JC5tewaR7s+grcTojoB4PHM3rXW+QaT29aVaApsM40xXohzC+kzpTGczmQnYizysOBwop6I2p78suxOWuCcJDFpIeziAB6RlavT4sIJDrEglGmPXZoSikO/fkhyj7/HFNsDJ6j5biLiwEwd+9evYZtKP5paRiDg1u5t0IIIToqmUIpREtTCnLWwca5sOVjsBfre6ml/g4G38zR0O4szPz4pOHt36P+XRPQJJB5fZqRc8qqikcdLr0sf63RtN355RwstFHlqfmlVdcgP3pGBnBzamydEbUwf7OsVzpHeJxOHFu3Yl+fgS1jPbZ16/FUBzbXwSwCLx9NwEUX4z9sKKaotlMsRwghxLlLApwQTan4gF6YZNM8vVqkjx/0HQuJ46HnJRx2FDBn+xwWfL+AClcFZoMZp8dZ7zJd/LswvOvwVniAtu3TjJw6+5rllNj5y8ebWJGZj7+vj3d92uGymiInPgaNbuH+9I4MZMzAKO9oWo8If/zPsCy/aP+qiouxZ2zAnrEe2/oMHJs365tnA6a4OHyCgnCWlYHbDSYTxtAwgq+7tpV7LYQQQtSQn16EaCxHKWxbDBvnwYGf9WPxF8D5f9I3yvYLYlfxLt779XE+3/s5CsXo+NHcPvB29pfub7Cq4n0p97XSw7QtHo8i72glBworOFBk46ml2+psSg36tMiF63PwNxtJiAxgeEJYndG0uFArJmPTleUX7YdSCuf+/d7RNfv6DJx79+onTSb8+vcj5NZbsaQkY01ORinFnlGj9fAG4HJRumgREX+8G5+IiNZ7ECGEEKIWCXBCnA23C/Z8p4e2nZ9DlQPCEuCSv8GgmyAkHqUUqw+v5t2t7/Jzzs9YfCzc3PdmJvafSHSAPuVvQNgAoGWrKrY1Dpeb7GIbBwptHCyq+XqwyEZWka1OSf4T0YAtT1wu0x7PcR6nE8eWrd7RNXtGBu6iIgAMQUFYk5IIuuYarCnJ+A0ahMGvbmXX3OlPoDx1/7wpj4f8WW/Q5fFpLfYcQgghxMk0KsBpmnYF8ApgBN5SSs04QbsbgIVAqlJKKpSI9kkpyN2oh7YtC6EiHyyhkDwRBt8C0SmgaVR5qvjvvi94d8u7bC/aTqhfKPcm38vNfW4myDeo3mXH9hjboQObUopim4sDhRV6MCu0caA6oB0stNWZ7gjgbzYSG2qlZ4Q/l/SNJDbUSnyolbhQK7e+tZJDJY569+gabJHwdg7Sp0NmYF9fPR1yy5aa6ZDxcQSMHKmPrqWkYO7RA81w8pFY+4YN4HLVPehyYc/IaK5HEEIIIc7YWQc4TdOMwOvAKCAbWKNp2hKl1Lbj2gUC9wGrGtNRIVpNaQ5snq+X/s/fDkYz9L4CBo+HhFHgo+/zZXPZ+GT3J/xn23/IKc+hW6duPD7sca7qeRW+Rt9WfojmVeX2cKjEwYGiWiGt1khaeWVVnfadO/kSF2rl/IRw4sP0cBZX/fVkBUT+cnnfOmvgACwmIw9d3qdZn0+0PqUUzn37q0fXqqdD7tunnzSZsPTvT8iECd7pkD7h4Wd8jx6fftLEvRZCCCGaXmNG4NKA3UqpvQCaps0DrgG2HdfuKeBZ4KFG3EuIllVZDtuX6lUk9y0HFMSmw9gXYcB1YA31Ni2wF/Dh9g/5aOdHlDnLSI5M5i+pf+Gi2IswaB1n7dVRh8sbzg4WVY+iVX+fU2LHXau6o9nHQGyIhbhQK2ndQ72jaPFhVmJCrFjMZ7dn2rFqk6eqQinaP09lJY4tW/SwlrFBnw5ZXR3SGBSEJTmZoOuu06dDDhxYbzqkEEII0VE1JsBFA1m1XmcD6bUbaJqWAsQqpZZpmnbCAKdp2hRgCkBcXFwjuiREI3jcsO9HfYrk9qXgskFwPFz4V0i8CcJ61mm+r3Qf7219j6V7luLyuLgk7hImD5hMUmRSKz1A4xxfMCTruPVoRRV1q2WGWE3EhfkzODaYqwd39Y6ixYdZ6Rzoh6GZ9ku7NjlaAlsHVFVUhD0jwzu65tiyBVU9ndEcH0/ARRfVTIfs3v2U0yGFEEKIjqrZiphommYAXgQmn6qtUupN4E3QN/Jurj4J0aAjW/XQtnkBHM0F3yA9sCWOh7ihcNx0voy8DN7Z8g4/ZP2A2WDmmoRrmNR/Et2CurVO/8/AmRQMMRo0ugb7ER/qz+UDomqmOlYHtU5+si+dODv6dMh93rVr9vXrce7fD4BmMuE3cCAhkyZiTUnBkpSET1hY63ZYCCGEaEMaE+BygNhar2Oqjx0TCAwEfqhezxIFLNE07WopZCJa3dEjemDbNA8ObwaDj76e7YoZ+vo2U93pWG6Pm++zvmf21tlszN9IkG8Qv0/8Pbf0vYUwS9v54fJsCobEhfmTEBHAJX0jvQEtPsxK12CLlN8XTaLOdMhj1SFLSgAwBgdjSUkheNwNWFJS8BswAINvx14zKoQQQjRGYwLcGqCXpmnd0YPbeODWYyeVUqWAdxW5pmk/AH+W8CZajdOml/zfOA/2fAvKA12TYcxzMPAG8K9f9MBR5WDJniW8v+19DpQdICYghkfTH+WantdgNVmbpFufZuSc0ZqusykYEh/qzwW9wr3h7NiatNCTFAwR4mxVFRZWT4fUR9ccW7fWTIfs3p2ASy/RR9eSUzB37yZ/BoUQQogzcNYBTilVpWnaPcBX6NsIvKOU2qpp2pPAWqXUkqbqpBBnzePRN9feOE/fbNt5FDrF6JtsDx4PEQ1XLyxxlDB351zm7ZhHkaOIAWEDeP7C57ks7jKMhrMrwNGQTzNy6lRVzCmx88iizThcVQyKCT6jgiHxYf6kdQ/1hrS4UD2o+Zmarr9CHE95PDj37vWOrtky1uM6cBCong45aBCht0/CkpKCJTkZn5CQVu6xEEII0b5pSrWtJWdDhgxRa9fKIJ1opPxd+vTITfOhNAvMAdD/Whh8M8RfACcogJB1NIv3t77Pp7s/xeF2MCJ6BHcMvIMhnYc0yyjB8BnfNriv2fFC/c119kOLC6v+vpkLhghxPI/DgWPzZu/omn3DBtylpQAYQ0KwpKRgTUnGkpyC38ABGMzmVu6xEEII0f5omrZOKTWkoXPNVsREiBZXUQhbPtZL/x9aD5oBel4Clz4OfceC+cRTHrcUbOHdLe/yzcFvMGgGruxxJbf3v52EkIQm72Z2sY3luwpYviv/pOHtjQkpxErBENHKqgoK6oyuObZt9252be7Rg4BRl2FNTsGSkoy5m0yHFEIIIZqbBDjRvlVVwq4v9SmSmV+Dpwo6D4LRT8OgGyEw6oRv9SgPK3JW8O6Wd1l7ZC2BpkAmD5jMhH4TiLRGNlkX7U43K/cVsnxXPst35bMnvwKArkF+WM1GbE53vfdEB1sYM6hLk/VBiNOhPB6ce/Z4R9dsGRm4DlZPhzSb8Rs0iLDJt2NJTsGSnCTTIYUQQohWIAFOtD9KQdYqPbRtXQSOUgiIgqF366X/owae9O1Ot5Nle5fx3tb32FO6h87Wzvx5yJ+5odcNBJgDmqB7il1Hylm+K58fd+Wzen8RzioPvj4GhvYI49b0eC7sHU7PiAAWbzhUZw0cgMVk5KHLG16bJ8TZcOXlkfPAg8S89CI+ERHe4x67Hfvmzd7RNfuGjXiOTYcMDcWSkkzIzTdjSUnWq0PKdEghhBCi1UmAE+1H0V7Y+BFs+giK94GPBfpdpRcj6XERnKK4SJmzjAU7FzBn+xzy7fn0DunNMxc8wxXdr8BkaNwUxRKbk58y9WmRP2UWeMv19+4cwKSh8YzsHUFa99B6BUWOVZs8kyqUQpypgllvYF+3jrwXXiTg4ou9o2uObdugSq9aau7Zk06jR2FJ1tewmeLjZTqkEEII0QZJERPRttmLYesnenDLWglo0H0EDL5FD2++gae8xOGKw/xn239YuGshtiobQ7sM5Y4BdzCs67Cz/gG1yu1hY3YJP1avZduUXYJHQZDFxAUJ4YzsHc7I3hF0CbKc1fWFOFuqqgrX4cO4srNxZmVRuXMXxR9+qFdkrab5+uI3aKB37ZolSaZDCiGEEG2JFDER7UuVE3Z/oxcj2fUluJ0Q3kcvRpJ4EwTFnNZldhbtZPbW2Xy570sUisu7Xc7kAZPpF9bvrLp1qMSur2PLzGdFZgFljioMGgyODWbqpb0Y2TuCwTHBGKUipGhGSincJSW4srKqQ1o2ruwsnNnZuLKyceXmgrv+ukoAjEYCLrmEmBeeR5PpkEIIIUS7JAFOtA1K6ZUjN86DzQvBXgTWcBhypz5FsksSnMZomVKKlbkrmb11Nr8c+gWLj4Xxfcczsf9EugZ0PaMuOVxuVu0r4sedemjbnVcOQJcgP8YM7MLI3hGcnxBGsFV+EBZNy+Nw4MrJwZmVpYey7Gw9oGVn48rKwmOz1WlvDAvDFBONZfBgOo0dizk2BlNMDJrFysFJk1CVlXpDt5uK5ctxl5bWWQsnhBBCiPZDApxoXSUH9b3aNs6Dwkww+kLf3+jFSBIuBePprU1zeVx8tf8r3tv6HjuKdhBuCee+lPu4sfeNBPkGndY1lFJk5tUqPrKviMoqD2YfA+ndQxmfGsuFvSNIiAyQtUGiUZTbTVVeXq0RtGyc2TVhrSo/v057zc9PD2XRMVjT0jDHRGOKjcUUHYM5JhqDv3+D98md/gSq1tRJ0CtN5s96gy6PT2u25xNCCCFE85EAJ1qeowy2LdaLkez/ST8WNxyG36Nvtm0JPu1LVbgq+HjXx3yw/QNyK3LpHtSdJ4Y/wZU9rsRsPPXIWKnNxYrdBd6pkbmlevGRhMgAJqTHc2GfCNIbKD4ixKm4y8r0EbTsHH2K47Hvs7JwHTqEqt5LDQCDAZ+ozphjYvEfMcI7gmaKicEcG4sxLOysfmlg37DBu2ebl8uFPSOjkU8nhBBCiNYiRUxE09s0H759Ekqz9fVql06DAdfD3u/1kbYdn0GVA0J76MVIEm+CkG5ndIt8Wz5zts9h/q75HHUeJSUyhTsG3sHImJEYNMMJ3+f2KDZml3j3ZNuQpRcfCfTzYUSvcEb2imBE7wiig6X4iDg55XTiOnRIH0HLya433dFTVlanvTEoSA9lsbH6CFpMLKbYGMwxMZi6dJE1aUIIIYTwOlkREwlwomltmg9Lp4LLXnPM4KOX/HceBb9gGHiDHtxihpzWurba9pbsZfbW2Xy29zOqPFVcFn8ZkwdMJjEi8YTvyS2189OuAn7clc+K3QWU2l1oGgyOCWZk7wgu7B3O4JhgfIwnDn7i3KOUoio/v8ERNGdODlWHD+trN6tpJlOtUTOvqrZXAAAgAElEQVR9uqMpVh9BM8XEYAw8dcVUIYQQQgiQKpSiJX37ZN3wBuCp0v+5+QPoNRp8fM/okkop1h1Zx+yts/kx+0d8jb5c3+t6JvWfRFynuHrtHS43q/cVeadF7jqiFx/p3MmX0f07M7J3BBckhBPiLyMe5zp3eQWunGxvyf06BUNyclAOR532PpGRmGJj8U9LrTuCFhuLT0QEmkF+CSCEEEKI5iUBTjSt0uyGj1c59H3bzoDb4+bbg98ye+tsNhdsJtg3mLsH3834vuMJ9Qv1tlNKsSe/3Lsn26p9hThcevGRtG6h3HheLCN7R9C7sxQf6ShceXnkPPAgMS+9eNJqit490bJqldnPzsJZPZLmLi6u097g748pNhbfHt0JGDGizgiaKToag++Z/fJBCCGEEKKpSYATTSdvBxgM4GlgD6rT3LsNwF5lZ/Huxby/7X2yjmYRGxjLY+mPcU3CNVh89LVppXYXv+wuYHlmPj/uzOdQdfGRHhH+3JIWx8jeEQztHobFLMVHOqKCWW9gX7eO/FmziLj33poRNO90x+qS+8fviebjg6lrV8wx0fiNGlUzghYTiykmGmNwsIR8IYQQQrRpsgZONI1dX8HC3+rfu53grqw5Z7LAVa/qxUpOoshRxLwd85i3Yx7FlcUMCh/E5AGTuTTuUsDA5pxS755sG7JKcHsUgb4+nJ8QzsjeEYzsHU5MiLX5nlG0KqUUzj17KPvvNxT8859wXHn8Y4xhYd5pjaaYaH0ELVpfl+bTuTOaj/zeSgghhBBtm6yBE81HKfjlVfjv49AlEcZ/CAd+qV+F8iTh7WDZQd7f9j6f7v6USnclF8VcxO0DbifGMoDlmQXc98tGVuwuoMSmFx9JjA7ijxf15MLeESTFSvGRjkophXP/fmyrVmNbvYqK1WtwFxTUbWQwYElKIuyuu/SwFhODwSohXgghhBAdl4zAibPncsBnf4KNc2HAdXDNLDCf/g/Pm/I3MXvrbL458A0+Bh9+0/1KBgdeQ2aOleW78tlx+CgAkYG+1SNsevGRUCk+0iEppXBlZ2NbtYqKVauxrVpFVV4eoBcPsaan49e/H/kvvYxyOr3v03x9SfjmvyddCyeEEEII0Z7ICJxoekePwEcTIHsNXPwYjHzotLYE8CgPy7OX8+6Wd1mftx5/n0BSQ27AUTiMhV+5+cB1GLPRQGr3EB4Z05eRvSPoGxUo65I6KFduLhWrVmFbtZqKVSupOpQL6NMg/dPTsKalY01Pw9ytG5qmkTv9CY7/pZPyeMif9QZdHp/WGo8ghBBCCNGiJMCJM5e7EebeAvZiuOl96H9NndOfZuQw86udHCqx0zXYwkOX92FMYjjL9i7jnc3vcuDofixaOL6l13H4cBKHPb70CDczPlVfxza0RxhWs/zR7IhceXk1UyJXrcZ18CCgb3JtTUvDeudv8R+ajrlnzwZDu33DBnC5jruoC3tGRkt0XwghhBCi1ckUSnFmtn4Cn9wN1jC4Za6+7q2WTzNyePTr99BCv0AzlaCqglD2ePw67cdFGR5HVyoLR+JXmczwnpFc2CeCkb0iiA2VdUsdUVVhIbbVq72jbM59+wAwBAZiTU3VR9nS0/Ht3Vv2UBNCCCGEqCZTKEXjeTzw47Pw4wyISYPxcyAgsl6zv/84B0PkQjSDPkqimUrBtAmHozNd3ZO5vMcILhwdSXJcMCYpPtLhuEtKqFizRh9lW7WKysxMAAxWK5bUIQSPG6evZevXF80oWzwIIYQQQpypRgU4TdOuAF4BjMBbSqkZx53/A/A/gBsoB6YopbY15p6iFTgr4NO7YdtiSJoAV74EPg1vaGzzX4rB4Kp3XDNU8s3vf9fcPRUtzH30KLY1a/XCI6tXU7ljByiF5ueHNSWFTldeiX96Gn4DBqCZTK3dXSGEEEKIdu+sA5ymaUbgdWAUkA2s0TRtyXEB7UOl1L+q218NvAhc0Yj+ipZWmq2vdzuyBUY/DcPuOWGxks+37kUzlTR4znCC46J98VRUYFu/3lsp0rF1K3g8aGYzluRkwu+9B//0dCyDBqGZpVqoEEIIIURTa8wIXBqwWym1F0DTtHnANYA3wCmlymq19wfa1oI7cXJZq2HeBKhywC0fQe/RDTZzVnl4dNkXfJE3E8MJBlmCzPWnW4q2z2O3Y8/I8Jb1t2/ZAlVVYDJhSUwk/A+/x5qWjiU5CYNvw6OyQgghhBCi6TQmwEUDWbVeZwPpxzfSNO1/gAcAM3BJQxfSNG0KMAUgLi6uEV0STWbDh7D0PugUDbcvhci+DTbbX1DO5I//SYF5PlbfTtw2YArvbX0Pl6r0tjFpvjwy9IGW6rloBI/TiX3DBmwrV1GxehWOjZtQLhcYjVgGDiTszjuxpqdhTU6WDbOFEEIIIVpBsxcxUUq9DryuadqtwN+A2xto8ybwJuhVKJu7T+IkPG74Zjr88ip0Hwk3vgfW0AabLly/h+m/PIkWuJ4+nc7j/8a8SKhfKAkhPXhl/SscrjhMlH8U96Xcx9geY1v2OcRpUU4n9i1bvFMi7RkZqMpKMBjw69ePkIkT8R+ajiXlPIwB/q3dXSGEEEKIc15jAlwOEFvrdUz1sROZB7zRiPuJ5uYog4/vgsyvIPV3cMU/wFh/TqTd6ebPi7/i++IXMAbkM7HPFB5M+yNGg15VcGyPsRLY2ihVVYVj61bvlEjb+vUoux0A3759CRl/M9b0dKxDhmDs1KmVeyuEEEIIIY7XmAC3BuilaVp39OA2Hri1dgNN03oppTKrX44FMhFtU9FevVhJQSaMfQFS72qw2Y7DZdy18N+U+M/F32Ll5Uv+zfnRw1q4s+J0Kbcbx44d3rL+trVr8VRUAODbK4Hg66/Xp0SmpuITEtLKvRVCCCGEEKdy1gFOKVWlado9wFfo2wi8o5Taqmnak8BapdQS4B5N0y4DXEAxDUyfFG3AvuUwf5L+/aRP9amTx1FK8d7KTJ5b8yzGoNX07pTIvy9/mQhrRAt3VpyM8niozMz0Tom0rVmDp0yvJWTu1s1b1t+aloZPeHgr91YIIYQQQpwpTam2teRsyJAhau3ata3djXPHmrfhi79AaE+4dR6E9qjXpNTu4v6P/8vKipcx+uVya+87eCh9Kj4G2Qe+tSmlcO7dS8WqVdhWrsK2Zg3u4mIATLGxWNPT8E9Px5qWhqlz51burRBCCCGEOB2apq1TSg1p6Jz8BH6ucrvgy4dhzVvQ63K44S3wq7/mKeNgMX9Y9C4VnT7EajXzwkWvc2Fs/RE60TKUUrgOHPCuYatYsxp3fgEAPl26EDByJNb0dPzT0zBFR7dyb4UQQgghRFOTAHcushXBgtv1qZPDp8Jl06G6AMkxHo/ijeU7eW3Dy/iE/ExCp/68MeplugR0aZUud3SuvDxyHniQmJdexCei7rRUZ3YOtlUr9VG2VaupOnIEAJ+ICPzTh3pH2UyxsWgn2GRdCCGEEEJ0DBLgzjX5O+HDm6EsB679FyTdUr/J0Urumf8tm12v4ROSxU29buXh9D9jaqAipWgaBbPewL5uHfmz3iD891Nq1rCtWoUrRy/uagwNrTUlMh1z924S2IQQQgghzjES4M4lu76Gj38LPn4weRnEptVrsiKzgKlL5uAMmYPVX+MfI15kVLdRrdDZjkW53Xjsdjw2G8pu937vsdlxHc6lZOFCUIqSefMomTsXAGNQENa0VEInT8aanoZvr14S2IQQQgghznES4M4FSsGvr8HX/wtRg+CWuRAUU6eJy+3hxf9u551tb2AO/5Eegb15/bKXie0Ue4KLdjzK48Fjs6PsNj1geUNW7dBlx2OvdcxWfdxuQ9mq29vrH1NO52n3wy85mS7T/hffPn3QDIZmfGIhhBBCCNHeSIDr6Koq4bP7YcMc6H8NXPsGmP3rNMkutvHHj74nU/0Lc9h+rku4gceGPoKv0fesb3uyNV2NoTyemjBVHaC8gat6RMtjrxW4Kmw1Yap24LLZagJWdVvlcJxRXzSTCc1qxWCxYDj21WLBGBqCyRJdc8xqQbNYMFis+rFarz2VDg7d/0BNwFOKym3b8AkPl/AmhBBCCCHqkQDXkZXnwUe3QdYquOgRGPkXOC4UfLkll4c+W4iKnIPV5OaJ8//BlT2ubPSt8197Dfu6dRyZOZOwu+6qO4J1LDydIlDpr221RrnsKLv9zDri41MnXGlWPTgZg4MxdelS59jJApfBUn3MWtNO82n8X5/c6U9w/FYeyuMhf9YbdHl8WqOvL4QQQgghOhYJcB1V7kaYeyvYCuHG2TDgujqnHS43T3+2lfl73sU36lviA7vxz0tfpkdw/X3gzoTHZiPvxZconb8AgLIlSylbsvTkbzIa6wYsq78+khUQiCEysiY4WY4LWNXtagJW3cBlsFjQzOZGPU9zs2/YAC5X3YMuF/aMjNbpkBBCCCGEaNMkwHVE2xbDJ38ASwjc+SV0TapzendeOXfP/ZFsn7fwjdjN2O5XMm3Y/2I1Wc/6lkopjn71NUeefZaq3FzQNH3tndGI/9ChhEy8rSaE1R7R8vfXpyKeo8U5enz6SWt3QQghhBBCtCMS4DoSpWD5TPj+7xCTCjfPgcDOtU4rFq7LZtpXS/GJmoPF5OBvw57guoTrGhWgKvfs4cjf/07FL79i7tkTzWRCHRtVcruxrV1L1xn/aNK1cEIIIYQQQpyLJMB1FE4bLP4jbP0EBt8CV74MJj/v6fLKKh5btJEvsj7CN/orogOieeWSd+gT2uesb+kur6DgjVkUvfc+BouFzn/7G5WZuyg5eLBOO1nTJYQQQgghRNOQANcRlObAvFsgdxOMegqG36tPYay2JaeUP879iXzLe/h23sGo+NE8OfwJAswBZ3U7pRRlyz4n77nnqMrLI+iG64l84AF8wsLYe+11sqZLCCGEEEKIZiIBrr3LWgMfTdBH4G6ZB32u8J5SSvHuz/uZ8f3X+HWdg6/pKH9JfYRb+t5y1lMmHbt2ceSpp7GtWYNf//7EvPoKlqSaNXaypksIIYQQQojmIwGuPds4D5ZMhU5dYNJiiOznPVVU4eTPCzbw05FPscR9TmdrJC9e9DqDIgad1a3cR49S8NprFH0wB2NAAFHTpxN84zg0o7GpnkYIIYQQQghxChLg2iOPG759En5+GbqNgJveB2uo9/SqvYVM/WglRwM/xC9qEyNiRvLMBc8Q5Bt0xrdSSlG6eDF5z7+Au7CQ4JtuIuJP9+ETEtKUTySEEEIIIYQ4DRLg2htHGSz6Hez6EobcCWOeA6MJALdH8c/vMnltxY/4x87FZCriTykPcPuA2zFohlNcuIFbbd/O4aeexr5+PX6DE4l94w0sgwY29RMJIYQQQgghTpMEuPakaB/MvQUKdsFvnoe033lPHS51MHXeejKKv8K/+1JCLcE8f+HbnNf5vDO+jbu0lPxXXqV43jyMQUF0+fvTBF13HZrhzEOgEEIIIYQQoulIgGsv9v0E8yeB8sDERdDjIu+p73Yc4YEFq3GFLMSvy3rSugxlxogZhFnCzugWyuOhdNEi8l54EXdpKSG33ELE1HsxBp351EshhBBCCCFE05MA1x6sfRc+/zOE9tArTYb1BMBZ5eHZL3fw7upVBHWbi9F4mLsH/5EpiVMwGs6suIh98xYOP/UUjk2bsJx3HlH/+zf8+vZtjqcRQgghhBBCnCUJcG2Zuwq+egRWvwkJo2Dc2+Cnj4btL6jg3rkZbD/6A516fkqgr5VnR/6bYV2HndEtqoqLyX/pZUoWLMAYFkbXZ2fQ6eqrz3qbASGEEEIIIUTzkQDXVtmKYMFk2PcjDLsHRj0J1aNqizfk8Ogn6zFELMESvZLBkSnMvHAmkdbI0768crspWbCA/Jdexl1eTuikSYTfew/GgLPb3FsIIYQQQgjR/CTAtUX5u2DuzVCaDdfMguQJANicVUxfspUFGzcS1mMelYYs7hx4J/cm34uP4fQ/SltGBkeeehrHtm1Y09KI+t+/4durV3M9jRBCCCGEEKKJNCrAaZp2BfAKYATeUkrNOO78A8BdQBWQD9yplDrQmHt2eJnfwMI7wccMt38GcekAbM8t454P13PAsYqQXovwNfnwwgWvcWHshad96arCQvJeeJHSRYvw6dyZ6BdfIHDMGJkuKYQQQgghRDtx1gFO0zQj8DowCsgG1miatkQpta1WswxgiFLKpmna3cBzwM2N6XCHpRSsnAVf/w0iB8AtcyE4FqUUH6w6yFOfbcIa9SWW8OX0DRvE8xc+T9eArqd36aoqiufOI//VV/E4HITd9VvC774bg79/Mz+UEEIIIYQQoik1ZgQuDditlNoLoGnaPOAawBvglFLf12q/EritEffruKoq4bMHYMMH0O8quO7fYPan1Obi4UWb+HLHDiIT5mPT9jKh3wQePO9BTNWbd5+Kbe1aDj/1NJU7d+I/fDid//YYvj16NPMDCSGEEEIIIZpDYwJcNJBV63U2kH6S9r8FvmjohKZpU4ApAHFxcY3oUjtUng8f3QZZK+HCv8KFD4PBwLoDxUydm0G+ewPhfRaiGT28MPwFRncbfVqXdeXlkff885QtWYpP1y5Ev/oKgaNGyXRJIYQQQggh2rEWKWKiadptwBCgwQVbSqk3gTcBhgwZolqiT23C4c0w9xaoKIBx78LA6/F4FP/6YTcvfL2dkJjv8PX/hvigPrxw0QvEd4o/5SWVy0XRB3MoeO01lNNJ2N1/IHzKFAwWSws8kBBCCCGEEKI5NSbA5QCxtV7HVB+rQ9O0y4DHgAuVUpWNuF/Hsn0pLPq9vq/bnV9A12Tyjjp44KON/Lx/L9F9F1GqdnBDrxt4OO1h/Hz8TnnJipWrOPz0Uzh378H/wpFEPfoo5vhThz4hhBBCCCFE+9CYALcG6KVpWnf04DYeuLV2A03TkoF/A1copfIaca+OQylY/jx8/zRED4HxcyAwiuW78nlg/gYqDDvo3Hc+lZqDZ4Y+w1U9rzrlJV2HD3Pk2Wc5+sWXmGJiiJk1i4CLL5LpkkIIIYQQQnQwZx3glFJVmqbdA3yFvo3AO0qprZqmPQmsVUotAWYCAcCC6jBxUCl1dRP0u31y2mDJPbDlY0i8Ga56FZfBzItf7uCNHzLpEv8zJuvndA7ozgsXvkBCSMJJL6ecTgrfe4+CN/4Fbjfh995D2G9/i8Hv1KN1QgghhBBCiPanUWvglFKfA58fd2xare8va8z1O5SyQ/p6t9yNcNl0OP9PZBXbmTrvVzbkZNNj4GLy3ZsZ22Ms04ZOw2qynvRy5St+5sjTT+Pcv5+Ayy6l88MPY46JaZFHEUIIIYQQQrSOFilics7LXgfzbgVnub6/W58xfLE5l798vAnM++g64CNK3WVMGzaNcb3GnXTqoysnhyMzZnD0v99gjo8n9v/eJGDEiBZ8GCGEEEIIIURrkQDX3DYtgMX/A4FRMPETHKF9eOqTzcxZdYD4Hmso8VtMkF9X3rpwFv3C+p3wMp7KSgrffpvCN/8PNI2I++8n9I7JGMzmFnwYIYQQQgghRGuSANdcPB747klY8RLEXwA3vc/uCjP3vP4zO/KO0Hfw5+Q41zIqbhRPDH+CQHPgCS919IcfOPLMP3AdPEjgFVfQ+S8PYeratQUfRgghhBBCCNEWSIBrDpVHYdEU2Pk5nDcZNeY5FmTkMW3JaiwBucQnzuWIs4C/pv6VCf0mnHDKpPPgQY488w/Kf/gBc8+exL37Dv7DhrXwwwghhBBCCCHaCglwTa14v16sJH8njJnJ0cTJPLZgK0s25tCn12byzQvw9Qln9qWzGRwxuMFLeOx2Cv/v/yh86200Hx8iH3qI0Im3ocl0SSGEEEIIIc5pEuCa0v6fYf5E8FTBbR+zyTeZe1/7meySEpJSvmKP/WdGdBnBMxc8Q7BfcL23K6Uo//ZbfbrkoUN0uvJKIh96CFPnyFZ4GCGEEEIIIURbIwGuqaybDcsehJDueMbP450dBp798hdCgwvpPngu+xw53JdyH3cOvBODZqj39sp9+zjy92eoWLEC3969if/P+1hTU1v+OYQQQgghhBBtlgS4xnJXwdePwap/Qc9LKRrzLx5cup/vd+aT1G8XWYY5eOjEW6PfIjWqfiDz2GwUvPEvCmfPxuDrS+dHHyXk1lvQfOSjEUIIIYQQQtQlKaEx7MWw4A7Y+z0M/R9+7Xkff3pzE8W2Coalf8+Wsm9J75zOjBEzCLeE13mrUoqjX37JkWefo+rwYYKuvZbIPz+IT3j4CW4mhBBCCCGEONdJgDtbBZkwdzwUH8B91T95pWgo/3xnLbGRR0noM5etZfv4w+A/8IfEP2A0GOu8tXL3bg7//e/Yfl2Jb/9+RL/4ItaU5FZ6ENFRuVwusrOzcTgcrd0VIYQQ5wg/Pz9iYmIwmUyt3RUhOiwJcGdj97f6yJvRRMG4hfzxJ19W78tkeOJB9qjZ4PLlX5f9i+HRw+u8zV1eQcHrr1P0n/9g8Pcn6vFpBN90E5rR2PB9hGiE7OxsAgMD6dat2wm3qhBCCCGailKKwsJCsrOz6d69e2t3R4gOSwLcqWyaD98+CaXZEBQD8cNh8wKIHMCKIa9yz8ICnO6jXHL+z6wpWkZyZDLPjXyOKP8o7yWUUpR99hl5z82kqqCA4HHjiHjgfnxCQlrxwURH53A4JLwJIYRoMZqmERYWRn5+fmt3RYgOTQLcyWyaD0ungsuuvy7Ngk0f4YlKYkbU87z5cS59op1YYuawpmgndwy4g3tT7sVkqJk24Ni5iyNPPYVt7Vr8Bg0i5vXXsCQmttIDiXONhDchhBAtSf6/I0TzkwB3Mt8+WRPeainIy+HN/XmMGpLPFue/OerQePXiV7k47mJvG3dZGfn/fI3iDz/EGBhI1FNPEnzDDWiG+lsICCGEEEIIIcTpkDRxEqo0u8HjQe4CrrxoLSsrXiC+Uzzzr5zvDW/K46Fk0SfsGfMbiufMIeTmm+j55ReE3HijhDchhBBCNGj//v0MHDiwWa79ww8/cOWVVwKwZMkSZsyYcdbX6tatG4MGDSIpKYkhQ4Y0VReFEGdAEsVJHCGcZf5WRsd0JbFbLKNjujInMIBJXbvy45GF3NL3Ft4f8z4xgTEA2Ldu5cCtE8h99FHMsbF0X7iAqGnTMAYHt/KTCHFqn2bkcP6M7+j+8DLOn/Edn2bkNOv9fvOb31BSUkJJSQmzZs3yHq/9g0ZbMnnyZLp3705SUhJJSUls2LChdTqyaT68NBCmB+tfN81v9lu2t8/qtddeIyEhAU3TKCgo8B5XSjF16lQSEhJITExk/fr1rdK/ZXuXMXrhaBLfS2T0wtEs27usWe/X3j6/E/1dayuf3zGuvDz23zaRqna23uvqq6/m4YcfbtQ1vv/+ezZs2MDatWubqFdCiDMhAe4k7vMdxvTwUHJNPihNI9fkw4ywELabfZg5ciaPpj+K2WjGXVJC7hNPsH/cjTizsujyj38Q/+Ec/Pr3b+1HEOK0fJqRwyOLNpNTYkcBOSV2Hlm0uVlD3Oeff05wcHC9HypbUlVV1Rm1nzlzJhs2bGDDhg0kJSU1U69O4ti63NIsQOlfl05t9hDX3j6r888/n2+++Yb4+Pg6x7/44gsyMzPJzMzkzTff5O67727qbp7Ssr3LmP7LdHIrclEocitymf7L9GYNce3t84OG/661hc+vtoJZb2Bft478WW802TWrqqqYMGEC/fr1Y9y4cdhsNp588klSU1MZOHAgU6ZMQSkFwKuvvkr//v1JTExk/PjxAFRUVHDnnXeSlpZGcnIyixcvrneP2bNnc8899wB6WJ46dSrDhw+nR48eLFy40Ntu5syZpKamkpiYyOOPP95kzyiEaDxZA3cS2yMPoI6f9qhp4Ankiu5X6NMlFy4k/8WXcB89SsjE24i4916MgYGt02EhTuCJpVvZdqjshOczDpbgdHvqHLO73Pxl4Sbmrj7Y4Hv6d+3E41cNOOE1Z86cia+vL1OnTuX+++9n48aNfPfdd3z33Xe8/fbb/Pzzz6xdu5aHH36YPXv2kJSUxKhRoxg7dizl5eWMGzeOLVu2cN555/HBBx+ccGF8t27duP3221m6dCkul4sFCxbQt29fioqKuPPOO9m7dy9Wq5U333yTxMREpk+fzp49e9i7dy9xcXH06dOHffv2sXfvXg4ePMhLL73EypUr+eKLL4iOjmbp0qUtt5/RFw/D4c0nPp+9BtyVdY+57LD4Hlj3XsPviRoEY04+XaqjfVbJyQ3vq7l48WImTZqEpmkMHTqUkpIScnNz6dKly0n//ZyJZ1c/y46iHSc8vyl/E06Ps84xh9vBtJ+nsXDXwgbf0ze0L39N++sJr9nRPr8TaYnPD+DwM89Quf3EnyGAcjqxb9oESlEybx6V27ejnaTvvv36EvXoo6e8986dO3n77bc5//zzufPOO5k1axb33HMP06ZNA2DixIl89tlnXHXVVcyYMYN9+/bh6+tLSUkJAH//+9+55JJLeOeddygpKSEtLY3LLrvspPfMzc1lxYoV7Nixg6uvvppx48bx9ddfk5mZyerVq1FKcfXVV7N8+XJGjhyJpmmMHj0aTdP4/e9/z5QpU075XEKIpiUjcCehfEoaPm48in3TJvbfPJ7D0x7HNyGB7osWEfXooxLeRLt0fHg71fHTMWLECH766ScA1q5dS3l5OS6Xi59++omRI0d6282YMYOePXuyYcMGZs6cCUBGRgYvv/wy27ZtY+/evfz8888nvVd4eDjr16/n7rvv5vnnnwfg8ccfJzk5mU2bNvHMM88wadIkb/tt27bxzTffMHfuXAD27NnDd999x5IlS7jtttu4+OKL2bx5MxaLhWXLakZGHnvsMRITE7n//vuprDwuSLWE48PbqY6fpo74WTUkJyeH2NhY7+uYmBhycpp3qvDxjg9vpzp+Ojri59fQ38SF0CwAACAASURBVLW28Pkd4zx0qO7rJupHbGws559/PgC33XYbK1as4Pvvvyc9PZ1Bgwbx3XffsXXrVgASExOZMGECH3zwAT4++u/jv/76a2bMmEFSUhIXXXQRDoeDgwcb/iXcMddeey0Gg4H+/ftz5MgR73W+/vprkpOTSUlJYceOHWRmZgKwYsUK1q9fzxdffMHrr7/O8uXLm+TZhRCnT0bgTqKLfxS5FbkElyv+9Kmbl6414jHAb1f4sn/GeHzCw+k6cyadrhwrZXNFm3aykTKA82d8R05J/Yqr0cEWPvr9sLO653nnnce6desoKyvD19eXlJQU1q5dy08//cSrr77KP/7xjxO+Ny0tjZgYfW1pUlIS+/fv54ILLjhh++uvv957z0WLFgH6Dxkff/wxAJdccgmFhYWUlemjkFdffTUWi8X7/jFjxmAymRg06P/Zu+/wuMor8ePfM6ORRr3Y6pItF7nihm3AgLFpNsE0G0ISQgopJLBJnCyQX7IbgtM2dUmAZANJFkI2ZcMGm2YIptnG2IAb2LgXSZZkNat3jWbe3x/3zmjUbNkqI4nzeR49o7lz5953Zu5I99zzvuedhdfr5ZprrgFg1qxZ5OfnA/DjH/+YtLQ02trauPPOO/npT38auCo+YM6QKeOX59ndJ7uIz4Y7zr0L3mj7rELpdJkygGX/WEZJY0m35enR6TxxzRPntM/R9vkNyXftNM6UKfOUl3Ps6mVgd2XEGHx1dWQ++J+EJSf3a99dzyVEhLvvvpsdO3aQnZ3NmjVraGlpAWD9+vVs3ryZ559/nh/96Efs3bsXYwxPP/00U6dO7bQdf2DWk4iIiMDv/u6Zxhi+/e1v86Uvfanb+pmZmQCkpKSwcuVK3n333U4XCpRSg08zcKex+vzVuJ1ubt7iY1ohfGOdl4ce87LovWaS7riDiS+9RPz112nwpka8+5ZPJdLl7LQs0uXkvuVTe3nGmblcLiZMmMAf//hHLr74YhYvXswbb7zB0aNHmT59+mmfG3xC4XQ6zzh+xr9+X9YFiI6O7vH5DocDl8sV+E47HI7A9tLT0xERIiIiuOOOO3j33XfPuJ8Bd+V3wRXZeZkr0lreD6Pts+pNZmYmhYUdAXBRUVHgZHSo+P+vBHM73aw+f/U5b3O0fX69fdeGw+cH1tg34+vcO8H4fAMyFu7EiRNs27YNgL/+9a+BYHrs2LE0NDQExqj5fD4KCwu5/PLL+elPf0ptbS0NDQ0sX76cRx55JBCI7d69+5zasXz5ch5//HEaGhoAK/tZXl5OY2Mj9fX1gDXebsOGDYNWOVMp1bt+BXAico2IHBKRoyLSraSRiFwmIrtEpF1EbunPvkJhxcQV/GDav3LFHoMDmFEEjkkTmPTcc6R+8z6cMdFn3IZSI8FN8zL58apZZCZEIliZtx+vmsVN8/p3crR48WJ+8YtfcNlll7F48WIeffRR5s2b1+miR2xsbOCEYCAtXryYv/zlL4BVbW/s2LHExcWd8/ZKSqysiTGGZ555JjQnLbNvhesftjJuiHV7/cPW8n4aTZ9Vb2644Qb+9Kc/YYzh7bffJj4+fsDHT53JiokrWHPxGtKj0xGE9Oh01ly8hhUTV/Rru6Pp8+vtuzYcPj+A5vfeA4+n80KPh+ZzDJaCTZ06ld/85jdMnz6d6upq7rrrLr74xS9y3nnnsXz5chYuXAiA1+vl9ttvZ9asWcybN4+vfe1rJCQkcP/99+PxeJg9ezYzZ87k/vvvP6d2LFu2jNtuu41FixYxa9YsbrnlFurr6ykrK+PSSy9lzpw5XHDBBaxYsSKQRVVKDZ1z7kIpIk7gN8DVQBGwXUSeM8bsD1rtBPBZ4N7+NDKU5rxwmBoJA9rB6SRj9oVETJoU6mYpNeBumpfZ74Ctq8WLF/OjH/2IRYsWER0djdvtZvHixZ3WGTNmDJdccgnnnXceH/nIR1ixon8nsn5r1qzhc5/7HLNnzyYqKoonn+ylyEcfffKTn6SiogJjDHPnzuXRRx8dkHaetdm3DkjA1tVo+qwefvhhfvazn1FaWsrs2bO59tpr+cMf/sC1117Liy++yOTJk4mKiuKJJ86ty2J/rZi4ot8BW1ej6fPr7bs2XD6/ic+sG5Tt5uTkcPBg9+IpP/zhD/nhD3/YbfmWLVu6LYuMjOSxxx7rtnzp0qUsXboUsCpPfvaznwWsipTB/Bk3gNWrV7N6dffM8Pvvv3+6l6GUGgLiT7Of9RNFFgFrjDHL7fvfBjDGdOtsLyJ/BF4wxvRcYivIggULzHCZV8Tfz90EFSuQiAgmv/pKv/u5KzXYDhw4cMbuU0oppdRA0/8/SvWfiOw0xizo6bH+dKHMBIJH0xfZy86aiNwpIjtEZEfFMJoQczD7uSullFJKKaXU2RoWVSiNMb8DfgdWBi7EzQkYzH7uSqmzs3LlSvLy8jot++lPf8ry5ctD1CLVG/2sRjb9/JRSanjrTwBXDGQH3c+yl40ag9XPXamhYowZNVVS163T7+NIoZ/VyKafn+qPcx2ao5Tqu/50odwO5IrIBBEJBz4OPDcwzVJK9Zfb7aayslL/mSqllBoSxhgqKytxu91nXlkpdc7OOQNnjGkXka8ALwNO4HFjzD4R+T6wwxjznIgsBNYBicD1IvI9Y8zpZxRWSg2IrKwsioqKGE7jSpVSSo1ubrc7MDm8UmpwnHMVysEynKpQKqWUUkoppdRQG6wqlEoppZRSSimlhpAGcEoppZRSSik1QmgAp5RSSimllFIjxLAbAyciFUBBqNvRg7HAqVA3Qo1qeoypwaTHlxpMenypwaTHlxpMw/X4Gm+MSe7pgWEXwA1XIrKjt4GESg0EPcbUYNLjSw0mPb7UYNLjSw2mkXh8aRdKpZRSSimllBohNIBTSimllFJKqRFCA7i++12oG6BGPT3G1GDS40sNJj2+1GDS40sNphF3fOkYOKWUUkoppZQaITQDp5RSSimllFIjhAZwSimllFJKKTVCaADXByJyjYgcEpGjIvKtULdHjR4iki0ib4jIfhHZJyKrQ90mNfqIiFNEdovIC6Fuixp9RCRBRP4hIgdF5ICILAp1m9ToISLfsP8/fiAifxMRd6jbpEYuEXlcRMpF5IOgZUki8oqIHLFvE0PZxr7QAO4MRMQJ/Ab4CDAD+ISIzAhtq9Qo0g7cY4yZAVwE/IseX2oQrAYOhLoRatR6CPinMWYaMAc91tQAEZFM4GvAAmPMeYAT+HhoW6VGuD8C13RZ9i3gNWNMLvCafX9Y0wDuzC4Ajhpjjhtj2oD/BW4McZvUKGGMKTHG7LJ/r8c68ckMbavUaCIiWcAK4A+hbosafUQkHrgM+G8AY0ybMaYmtK1So0wYECkiYUAUcDLE7VEjmDFmM1DVZfGNwJP2708CNw1po86BBnBnlgkUBt0vQk+w1SAQkRxgHvBOaFuiRplfAd8EfKFuiBqVJgAVwBN2N90/iEh0qBulRgdjTDHwC+AEUALUGmM2hLZVahRKNcaU2L+XAqmhbExfaACn1DAgIjHA08DXjTF1oW6PGh1E5Dqg3BizM9RtUaNWGHA+8FtjzDygkRHQ/UiNDPZYpBuxLhRkANEicntoW6VGM2PNrzbs51jTAO7MioHsoPtZ9jKlBoSIuLCCt78YY9aGuj1qVLkEuEFE8rG6f18hIn8ObZPUKFMEFBlj/D0H/oEV0Ck1EK4C8owxFcYYD7AWuDjEbVKjT5mIpAPYt+Uhbs8ZaQB3ZtuBXBGZICLhWINnnwtxm9QoISKCNXbkgDHmwVC3R40uxphvG2OyjDE5WH+7XjfG6NVrNWCMMaVAoYhMtRddCewPYZPU6HICuEhEouz/l1eiRXLUwHsO+Iz9+2eAZ0PYlj4JC3UDhjtjTLuIfAV4Gav60ePGmH0hbpYaPS4BPgXsFZH37GX/Zox5MYRtUkqps/FV4C/2Rc7jwB0hbo8aJYwx74jIP4BdWFWbdwO/C22r1EgmIn8DlgJjRaQIeAD4CfCUiHweKABuDV0L+0asrp5KKaWUUkoppYY77UKplFJKKaWUUiOEBnBKKaWUUkopNUJoAKeUUkoppZRSI4QGcEoppZRSSik1QmgAp5RSSimllFIjhAZwSimlRi0R8YrIe0E/3xrAbeeIyAcDtT2llFKqL3QeOKWUUqNZszFmbqgboZRSSg0UzcAppZT60BGRfBH5mYjsFZF3RWSyvTxHRF4XkT0i8pqIjLOXp4rIOhF53/652N6UU0R+LyL7RGSDiESG7EUppZT6UNAATiml1GgW2aUL5ceCHqs1xswCfg38yl72CPCkMWY28BfgYXv5w8AmY8wc4Hxgn708F/iNMWYmUAPcPMivRyml1IecGGNC3QallFJqUIhIgzEmpofl+cAVxpjjIuICSo0xY0TkFJBujPHYy0uMMWNFpALIMsa0Bm0jB3jFGJNr3/9/gMsY88PBf2VKKaU+rDQDp5RS6sPK9PL72WgN+t2Lji1XSik1yDSAU0op9WH1saDbbfbvW4GP279/EnjT/v014C4AEXGKSPxQNVIppZQKplcKlVJKjWaRIvJe0P1/GmP8UwkkisgerCzaJ+xlXwWeEJH7gArgDnv5auB3IvJ5rEzbXUDJoLdeKaWU6kLHwCmllPrQscfALTDGnAp1W5RSSqmzoV0olVJKKaWUUmqE0AycUkoppZRSSo0QmoFTSimllFJKqRFCAzillFJKKaWUGiE0gFNKqUEkIkZEJtu/Pyoi9/dl3XPYzydFZMO5tlNZROQtEZkX6naMZCKSLyJX2b//m4j8YRD2cdrv0lls53oR+ftAtEkppYaKBnBKKXUaIvJPEfl+D8tvFJFSEenzdCzGmC8bY34wAG3KsYO9wL6NMX8xxizr77Y/zETkeqDeGLPbvr9GRP4c4mZ1Y3/2e0XEEbTshyLyxxA2q0fGmP8wxnyhP9sQkc+KyJYu2x2Q75Ix5nlgpojM7u+2lFJqqGgAp5RSp/ckcLuISJflnwL+YoxpD0GbPjTOJkAeAF8G/mcI93daZ3jtGXRMOD5Y+/iw+BtwZ6gboZRSfaUBnFJKnd4zwBhgsX+BiCQC1wF/EpELRGSbiNSISImI/FpEwnvakIj8UUR+GHT/Pvs5J0Xkc13WXSEiu0WkTkQKRWRN0MOb7dsaEWkQkUVdsxQicrGIbBeRWvv24qDHNorID+zugvUiskFExvbS5kQReUFEKkSk2v49K+jxJBF5wn4N1SLyTNBjN4rIe/ZrOCYi19jLA13s7PuBTFdQdvHzInICeN1e/n92xrNWRDaLyMyg50eKyH+KSIH9+BZ72XoR+WqX17NHRFb28DrDgSuATT29Dz2s/y37NdWLyH7/NkUkXESqRGRW0LopItIkIsn2/evs96VGRLYGZ3/s9+b/iTXBeONpAqyfAd/r7XERuUFE9tn72Cgi00+zj8n2e36HfaxVi8iXRWSh/X7ViMivg54/SUReF5FKETklIn8RkYRe2hH82f7aPl79P+3+4/o07+d04FFgkf2cGnt51+/SF0XkqP3ePyciGUGPGfv1HLFfy29EOl2Q2Qis6OV9VkqpYUcDOKWUOg1jTDPwFPDpoMW3AgeNMe8DXuAbwFhgEXAlcPeZtmsHM/cCVwO5wFVdVmm095mAdXJ5l4jcZD92mX2bYIyJMcZs67LtJGA98DBW8PkgsF5ExgStdhtwB5AChNtt6YkDeAIYD4wDmoFfBz3+P0AUMNPe1i/tNlwA/Am4z34NlwH5vb0fPVgCTAeW2/dfwnqfUoBdwF+C1v0FMB+4GEgCvgn4sLOn/pVEZA6QifXedJUL+IwxRX1s3zGsoD4e+B7wZxFJN8a0Af8bvF/gE8BrxpgKscbXPQ58CeuzeQx4TkQiuqy/Auvz7S3DuxaoAz7b9QERmYKVVfo6kAy8CDwvnS8sBPYB+PdxIdb78DHgV8C/Yx2XM4FbRWSJfxfAj7GygNOBbGBNL+0MMMZ8xT5eY4BLgWrgWfvh3t7PA1iZ0W32c7sFiiJyhd2eW4F0oADrMwh2HbAQmG2vtzzosQNAjojEnek1KKXUcKABnFJKndmTwC0i4rbvf9pehjFmpzHmbWNMuzEmH+uEfEnPm+nkVuAJY8wHxphGupwAG2M2GmP2GmN8xpg9WCfkfdkuWCfmR4wx/2O362/AQeD6oHWeMMYcDgpQ5/a0IWNMpTHmaWNMkzGmHviRvx0ikg58BPiyMabaGOMxxvgzWJ8HHjfGvGK/hmJjzME+th9gjTGm0W4fxpjHjTH1xphWrPdqjojEizUO7HPAansfXmPMVnu954ApIpJrb/NTwN/tIKurBKC+r40zxvyfMeak/dr+DhwBLrAffhL4RFCW51N0dM28E3jMGPOO3dYngVbgoqDNP2yMKfS/9t6aANwP3C/dM74fA9bb770HK8CNxApwT7ePHxhjWowxG7AuIPzNGFNujCkG3gTm2a/9qL3tVmNMBdYFgr4em9iZyGeAr/rHG57h/TyTT2Ida7vsz/3bWBm7nKB1fmKMqTHGnADeoPPx7v/ce8wiKqXUcKMBnFJKnYExZgtwCrhJRCZhnVj+Faxsh1jdCktFpA74D6xs3JlkAIVB9wuCHxSRC0XkDbG6LtZiZSH6sl3/tgu6LCvAyj75lQb93gTE9LQhEYkSkcfs7ol1WN03E0TEiZV5qTLGVPfw1GysrMq5Crw3IuIUkZ/YXezq6MjkjbV/3D3tyxjTAvwdawyjAyvr1NsYt2ogtq+NE5FPB3WDrAHOs9uCMeYdrPd0qYhMAyZjBZNgZTLv8T/Pfm421mfW7bWfjjHmRaAIK5sXrNPnb4zx2dsM/vx72kdZ0O/NPdyPARCRVBH5XxEptj+PP9PHY1NEXMA/gL8aY/43aHmv72cfdH29DUAlfT/e/Z97TR/3p5RSIaUBnFJK9c2fsDJvtwMvG2P8J7e/xcpu5Rpj4oB/w+pidiYlWCfufuO6PP5XrJP+bGNMPNY4IP92zRm2fRIrUAg2DijuQ7u6ugeYClxovz5/903BCgKSehn/VAhM6mWbjVjdLv3Selgn+DXeBtyI1Z0vHsgJasMpoOU0+3oSK0NzJdDUtbtpkKOAiEhmL48HiMh44PfAV4Axdre+D+j8ufu7b34K+IcdTIL1vvzIGJMQ9BNlZ0n9zvT5Bvt3rGMu+P3s9PnbmcBsOn/+Z7OPrv7Dfv4s+5i4nb4d8wCPYHX9/E5Q+870fp7V8S4i0VjdU/t6vE8H8o0xdX1cXymlQkoDOKWU6ps/YQUQX8TuPmmLxTohbbCzLXf1cXtPAZ8VkRkiEgU80OXxWKzsVos9nuy2oMcqsMZ4Texl2y9idR28TUTCRORjwAzghT62rWs7mrEKpiQFt9MYU4I1Nu2/xCp24hIRf4D338AdInKliDhEJNN+fwDeAz5ur78AuKUPbWjFyqpEYQUQ/jb4sMaUPSgiGXa2bpF/TJkdsPmA/+Q0FSbtbpWv0r0roENE3EE/EUA0VlBRASAid2BljIL9GViJFdz8KWj574Ev2xlWEZFosQrW9Dn716XdG7GCnc8ELX4KWGG/9y6sILwV2Hou++hBLNAA1NoB7319eZKIfAnr/f2k/bn5nen9LAOyeugq6vc3rGNtrv35/Afwjt2luS+WYB3HSik1ImgAp5RSfWCfDG7FOtl8Luihe7GCq3qsk/M+TQpsjHkJq1DE61jZn9e7rHI38H0RqQe+i3VS7n9uE9ZYtLfsLmfB46cwxlRiFW24Byvo+SZwnTHmVF/a1sWvsMZPnQLeBv7Z5fFPAR6sLGQ5VuEMjDHvYhVJ+SVQi1Xd0Z8luR8rY1aNVbDir2dow5+wusgVA/vtdgS7F9gLbAeqgJ/S+f/bn4BZWEHV6Txmv55gn8AKYP0/x4wx+7ECwm1YwcUs4K3gJxljCrGKrRis8WP+5TuwLgL8Guv1H6WHQiRn6TtYxVv8+ziEFTg+gvW5XQ9c38vYv3PxPeB8rM91PVZBlb74BNZFh5PSUYny3/rwfr4O7ANKRaTbMWyMeRXrmHoaK7M9ibObYuETWJ+9UkqNCGJMf3pRKKWUUsObiHwauNMYc2kf1n0L+Iq/uEY/9/s4cNIY850zrqxCQqzJ2z9ljLk11G1RSqm+0gBOKaXUqGV3T30d+C9jzJ/OtP4A7jcHq6voPGNM3lDtVyml1OinXSiVUkqNSiKyHGtcVRln7qY5kPv9Ada4tJ9r8KaUUmqgaQZOKaWUUkoppUYIzcAppZRSSiml1AgRFuoGdDV27FiTk5MT6mYopZRSSimlVEjs3LnzlDEmuafHhl0Al5OTw44dO0LdDKWUUkoppZQKCREp6O0x7UKplFJKKaWUUiOEBnBKKaWUUkopNUJoAKeUUkoppZRSI4QGcEoppZRSSik1QmgAp5RSSimllFIjhAZwSimllFJKKTVCaACnlFJKKTUE1h9fz7J/LGP2k7NZ9o9lrD++PtRNUkqNQMNuHjillFJKqdFm/fH1rNm6hhZvCwAljSWs2boGgBUTV4SwZUqpkUYzcEoppZRSg+yhXQ8Fgje/Fm8LD+16KEQtUkqNVBrAKaWUUkoNIo/XQ0ljSY+PlTSWcLT66BC3SCk1kmkAp5RSSik1CIwxvFrwKjc+e+Np11v53Epuf/F21h5ZS5OnaYhap5QaqTSAU0oppZQaYPtO7eOz//ws39j4DcId4dwx8w7cTnenddxON9+56Dvcu+Be6tvqeWDrA1z+1OU8sPUB3q94H2NMiFqvlBrOtIiJUkoppdQAKW0s5eFdD/P88edJcidx/0X3syp3FWGOMKYmTeWhXQ9R2lhKWnQaq89fHShg8ukZn+b9ivdZe2QtL+W9xNoja5mcMJlVuau4buJ1JLoTQ/zKlFLDhQy3qzsLFiwwO3bsCHUzlFJKKaX6rMnTxOMfPM6T+57EZ3zcPuN2vjDrC8SGx571thraGvhn/j9Zd2Qde07tweVwccW4K1g1eRUXZVyEQ7QDlVKjnYjsNMYs6PExDeCUUkoppc6N1+fluWPP8fDuhznVfIqP5HyE1fNXkxmTOSDbP1x9mHVH1vH88eepba0lIzqDm3JvYuXklaRFpw3IPpRSw48GcEoppZRSA+ztkrf5xfZfcKj6ELOTZ3PfgvuYmzJ3UPbV5m3j9ROvs/bIWraVbEMQLs68mJtzb2Zp1lJcTteg7FcpFRoawCmllFJKDZC82jwe3PEgG4s2khGdwTfmf4PlOcsRkSHZf1F9Ec8cfYZ1R9dR3lROkjuJ6ydez6rcVUxMmDgkbVBKDS4N4JRSSiml+qmmpYbfvv9bnjr0FBFhEXxx1he5fcbtRDgjQtIer8/L1pNbWXtkLRsLN9Ju2pmbPJdVuatYnrOcKFdUSNqllOo/DeCUUkoppc5Rm7eNvx38G4/teYxGTyO35N7C3XPvZkzkmFA3LeBU8yleOPYCTx95mvy6fKJd0VyTcw03597MeWPPG7LsoFJqYGgAp5RSSil1lowxvHriVX6585cU1hdySeYl3Dv/XiYnTg5103pljGF3+W7WHlnLhoINNLc3k5uYy6rJ1nQECe6EUDdRKdUHGsAppZRSSp2FD059wM+3/5xd5buYnDCZexbcw6WZl4a6WWeloa2Bl/JfYu3htXxQ+QEuh4urxl3FytyVXJh+oU5HoNQwpgGcUkoppVQflDaW8tCuh3jh+AskuZP4yryvsHLySsIcYaFuWr8cqjrEuqPreP7Y89S11ZEZk8nKySu5cfKNOh2B+lBaf3w9D+16iNLGUtKi01h9/mpWTFwR6mYFaACnlFJKKXUaXSfi/tSMT/GFWV8gJjwm1E0bUK3eVl4reI21R9fyTsk7OMTBJRmXcHPuzVyWfRkuh05HoEa/9cfXs2brGlq8LYFlbqebNRevGTZBnAZwSimllFI98Pq8PHvsWR7Z/Yg1EfeEj7D6/IGbiHs4K6wvZN2RdTx79FnKm63pCG6cdCMrc1cyIX5CqJunVL81eZoobyqnrKmMsqYyypvKKW0s5Zmjz9Dqbe22fnp0Ohtu2RCClnanAZxSSimlVBdvl7zNz7f/nMPVh5mTPIf7Ft7HnOQ5oW7WkGv3tbP15FaePvw0m4s2027aOT/lfFblruLq8VfrdARq2DHGUNdWZwVmjR3BWfD9sqYy6tvquz03LjyOura6HrcrCHs+s2ewm98nGsAppZRSStmO1x7nwR0PsqloE5kxmXx9/tdZPn7oJuIezk41n+K5Y8+x9shaCuoKiHZFc+2Ea7k592ZmjJmh75EadD7jo6qlirLGMkqbSilrLOuWRStrLOvU/RGs4GtM5BhSo1JJjUolJSqF1OjUwP3UaGtZZFgky/6xjJLGkm771gzcOdIATimllFKDobqlOjARd2RYJF+c/UU+Of2TIZuIezgzxrCrfJc1HUH+Blq8LUxNnMrK3JVcN/E64iPiQ91ENQJ5vB4qmiu6ZcqCg7SKpgraTXun54U5wkiJ7AjIUqJSAkGZP0AbGzW2z2M4PxRj4ETkGuAhwAn8wRjzky6Pfxn4F8ALNAB3GmP2i0gOcAA4ZK/6tjHmy6fblwZwSimllBpIgYm433+MxvZGPjrlo9w1565hNRH3cFbfVs9LeS/x9JGn2V+5n3BHOFeNv4pVuatYmLZQpyMYJp7ZXczPXz7EyZpmMhIiuW/5VG6aN3RjOYPHm/lvSxtLO2XNKlsquz0vMiyy16xZSrQVqCW5kwb8OBvVVShFxAkcBq4GioDtwCeMMfuD1okzxtTZv98A3G2MucYO4F4wxpzX18ZqAKeUUkqpgeCfiPvBHQ9S1FDEpZmXcs/8e4b1RNzD3cGqg6w9spYXjr9AfVs9WTFZrMpdxQ2TbiA1OjXUzfvQemZ3Md9eu5dmjzewLNLl5MerZvU7iOs63iy4O2NwJq238WadujEGdWX0/x7rfds8DwAAIABJREFUitWuuT3obwC3CFhjjFlu3/82gDHmx72s/wng08aYj2gAp5RSSqlQ6DoR970L7uWSzEtC3axRo6W9hVdPvMq6I+t4t/RdHOJgceZiVuau5LIsnY5gKNQ2edhTXMOeoloeef0I7ZE7iUh+GXHVYDwJtFYsJ957Idu+fSXhYT1nr4LHm/XUndF/vz/jzdS56W8AdwtwjTHmC/b9TwEXGmO+0mW9fwH+FQgHrjDGHLEDuH1YGbw64DvGmDd72MedwJ0A48aNm19QUHBWL1AppZRSCkbvRNzD2Ym6E6w7ak1HUNFcwRj3GG6cfCOrclcxPm58qJs3KjS2tvNBcS17i2t5v6iWvUU15Fc2BR4Pi9uNO30t4vAElhmfi9ay5bh945k5DsaneYiLaaS69VQgYOtxvJmEBQKyQKbM7s6YFpV21uPN1LkZkgAuaP3bgOXGmM+ISAQQY4ypFJH5wDPATH93y55oBk4ppZRSZ6vR0xiYiNsYw2dmfobPnfe5UTcR93DW7mtnS/EWnj7yNG8WvYnXeJmfOp+bc2/mqvFXaTamj1o8Xg6U1FnBWmEte4trOFregM8AeElLbCc3XchK9jI23kNMVAu/3v1f4Gg506YxPhduSSItOo0pYzMZH5/RLYM2GOPN1Nkb6i6UDqDaGNOtPJGIbATuNcb0GqFpAKeUUkqNDKEumgDWRNzPHH2GR3Y/QmVLJddOuJbV568mIyZjSNuhOqtoquDZY8+y7sg6TtSfIMYVw4qJK1iVu4oZY2aEunnDRlu7l/eLS3n3xAn2lBRxpLKE4rpTGGc94mzE7W4iOrIZh6sRD3U0tXcfZ3Ymv73qtyS7UyipiuDNgw1sOFBGYVUzIjB/XCLLZqaybEYaOWOjB+EVqnPV3wAuDKsL5JVAMVYRk9uMMfuC1sk1xhyxf78eeMAYs0BEkoEqY4xXRCYCbwKzjDFVve1PAzillFJq+BvMogl9te3kNn6x4xccrj7M3OS53LfwPmYnzx6Sfau+Mcawo2wHa4+s5ZWCV2j1tjItaRqrcldx7YRrR+V0BG3eNqpaqjr/NFdxqvkUBbXlFNVWcKq5kgZPDR7qEIe3x+3EuuIYGzWGJHcSSe4kxrjHkBRp39rLktxJJEUmcctzt/R5XjNjDIfK6nn5gzI27C9l30mrY9yU1BiWz0xj2Yw0zsuM08IiITYQ0whcC/wKaxqBx40xPxKR7wM7jDHPichDwFWAB6gGvmKM2SciNwPft5f7sAK750+3Lw3glFJKqeFv0Y9fo6S2e5etzIRI3vrWFYO6764TcX9j/jdYNn6ZnnAOc3Vtdbx4/EXWHlnLgaoDRDgjuGr8VdycezMLUhcM28/PZ3zUt9VT2VJJVXOVdRsUmAUHapXNldR7es6SGV8YxhuDaY/B4YslISKR9JhkJiSlMD05gynJ6YyNHEuSO4kEd8JZjTHrz7xmRdVNbNhnBXPv5lXhM5AR7+bqGaksn5nGwglJuJzapXKo6UTeSimlVB8M93mBQqHF4+VYRQOHSus5VFbP4dJ6DpXWc7KH4M3vOyumszAniZkZcYQN4IlfdUs1//Xef/F/h/+PyLBI7px9J7dNv00n4h6B9lfuZ+2Rtbx4/EXqPfWMix3HytyV3DjpRpKjkgd9/y3tLd0Cr97uV7dUdyv0AVYlxkR3IknuJGLCEsAbQ0trJDX1EZTVuGhqcuPzxhBm4pianM7czBRmZyUwJzuBSckxOB0DG7CuP76eJzY9yCf+WsJfb0vnc0v+9az/flU1tvH6wXJe3lfK5sMVtLb7iI90ceW0FJbNTOOyKWOJCteCQENBAzillFLqDPpzBXs08PoM+ZWNVoBWVh8I2PJPNdrFEyDc6WBicjTT0mJ5/WA5dS3dT2qdDsFrPyEq3Mn54xJZmJPEwgmJzMtOJDLcedZta/O28dcDf+V3e35HU3sTt0y5hbvn3k2SO6lfr1mFXnN7M68WvMraI2vZUbYDpzhZnLWYVZNXUd9Wz6/f+3WfLqh4fV5q22o7ZcQqWyq7BWb+n0ZPY4/biQyL7Oiu6E5iTGT37opOE8vJSid5ZfBBcT17imopr28FrON/Smosc7LimZUVz5ysBKakxvZaxn+glaz5HjV//zsJH/846Q98t1/bampr580jp3h5XymvHSinttlDRJiDxbnJLJuZylXTU0mKDh+glquuNIBTSimlTqPJ08Q1T19DdWt1t8fiI+L5/dW/Z1LCJMKdI/9kxRhDSW1LIEjzB2xHyhtoa/cBIAI5Y6KZkhrD1NRYpqbFMTUthvFjogNdqU43Bu6iiWPYnl/F9vwq3s2r4lBZPcaAyymclxnPBTlJLMhJYmFOIglRvb+nxhheKXiFB3c+SHFDMYszF3PPgnuYlDBpcN8kFRIFdQWsO7KOZ489y6nmU90edzlcXJF9BclRyZ26MVY2V1LTWoPP+Lo9xyEOEiMSuwViwff9Y8sSIxKJckV1en5di4cPimrZU1zLniJrzrWi6mbA+p5MHBvNnKwEZmXFMzsrgRnpced0keJsmfZ2PEVFtObl0ZZfQFteHq2HDtH8/vvWCk4nWY89Suyllw7I/tq9Pt7Nr2LDvjJe2V9GcU0zDoGFOUksm5nGshmpZCdFnXlDqs80gFNKKaW6KG8qZ1PRJjYWbuSdkndo9baedv0wCSMnPoepSVOZmmj9TEmawtjIsUPU4rNX3djWKZvmD9bqgzJnaXFupqTFMi0tlimpsUxNjWVySkyfTkL7WoWyttnDzoIq3s2rZkd+FXuKamnzWifbU1JjWJiTxAUTkliYk0RGglVqfm/FXn6+4+fsLt/N5ITJ3LfgPi7OvHiA3hk1nHl8Hq586soeL6gAxLhiumXFeivyER8R3+eS+M1tXvadrGVPkR2sFddyvKIjU5edFMnsrARmZ1rB2nmZccS6B28uNGMM3qoq2vLzrQAtL4+2vHzrfmEheDrmfHMmJIDTibe6Gnwdgax75kwSPnYrcdeuwBkzMFUmjTHsO1nHhn2lbNhfxsFSa8zfjPS4QEXL6emxw3ZM40ihAZxSSqkPPWMMh6oP8UbhG2wq3MS+SquYcmZMJpdnX86LeS9S1dK9SHJyZDLfvOCbHK46zKHqQxyqOkRZU1ng8THuMUxLmsaUpCmBwC4nPmdIJ45uamvncFlDt+6PFfUdQWl8pMvOpsUyJc0K1KamxhIfNfST8bZ4vLxfWGNl6PKr2VVQTUOrFVSmJzUTk/4Kpb6txIcnsvr8r7IqdxVOx+BnNdTwMfvJ2Ri6n6MKwp7P7On39tvafRwsresI1opqOVLeEOj+mxoX0RGsZVu3iYPUXdDX2hrIorXlW0Faq33rq+uYOllcLlzjxxExYQLhORMIz8khfMIEwifkYDwejl29DNMadCEqLIzw7Gza8vJwREURd911JHzsViJnzhzQ9uefauSV/VYRlB0F1RhjBbvLZliZuQU5SQM+3u/DQAM4pZRSH0pt3jbeLX2XjYUb2VS0idLGUgRhdvJslmYvZWnWUiYlTEJEzmoMXE1LTSCYO1R9iMPVhzlWcwyPz7oiHu4IZ1LCpI5sXdJUpiRO6XfJ9LZ2H3mnGu0grY5DpQ0cLqvnRFVTR5tdDqakdmTTpqZZPymxEcP2ini718fuojIefe8P7KhZh89naK1aTFvlUhLdMSzISeKCnCQWTrAKo2hFvNFv2T+W9bks/pm0e30crWjoFKwdLKkPZIETo1xWsGZ3g5ydFU9qnHtAXoef8floLy2lLT+/I5OWl0dbfj6ekych6Hw8LDXVCsxyxlvBmv3jyshAnD1fyChZ8z1qnn66U1YOl4uEW24m/oYbqHnq/6h76SVMSwvu884j4daPDmhWzq+ivpXXDpSxYX8ZW46cos3rIyk6nKump7BsRhqX5o7F7dKLMX2hAZxSSqkPjaqWKt4sepNNRZt4q/gtmtqbiAyLZFH6IpZmL2Vx1uJeuz32pwqlx+chrzaPQ1VWQOcP7oKzemnRaVbXy8QpgeBuXNy4bl28fD5DUXUzB0vrOFxWzyE7u3asooF2O0PgdAgTx0Zb3R9TO7Jq2UlRI+pqd9eJuFdMXMHX5n6NlpY4duRX8649lq6g0gpSo8KdzBuXYHW7zEli3rhzK4wSEnuegte+D7VFEJ8FV34XZt8a6lYNP8aw/o3vsKbgWVqCjmW3z7BmwkpWLP1Br0/12cV49hbX8n6hFbDtO1kXGKsZExHGrMz4TsFaVmLkgF3c8DY0WIGZv8tjfn6g26Np6bg45IiKCsqgWVm08JwcInJycESffVB1/KaVtB482G15xLRpTHxmndW2ujpqn3uemr//ndYjR6ys3PXXk3DrRwc8KwfQ0NrOpkMVbNhfyusHy6lvaScq3MmSKVYRlCumpoakB0DAMP8+agCnlFJq1DLGkFebx8aijWws3Mj7Fe/jMz5SIlNYkr2EpdlLuTD9wpCVmj/VfCoQzB2qsn7y6/LxGuuE0u2MJD1yAtGSja81nZrqsZwojae5rePEJisxsmOMmn07MTmaiLARErj0YtvJbfx8x885Un2EeSnzuG/BfcxKntXjumV1LVZhlDyr2+XB0jqMgTCHXRhlQhILxlsVLwerq1u/7HkKnv8aeJo7lrki4fqHB++k0RjwesDbBj6P/bv/frt1620Db/sZ1unp/pm2ay/zeXrZxxnWAdZHR/FQYgKlYU7S2r2srq5hBdHw9Q/A5cYYQ3FNM3uLannfzq7tLa4NjPF0uxzMzIhnVmY8c7KtgG3CmGgc/bzAYTwe2oqKOsaj+QO2/Hy8p4KKrzgcuLKyCJ+QQ0SOP0izArawlOSQZcSNMTS/996QZeXA6j3w9vFKNuwvZcO+MsrrWwlzCBdOTGL5zDSunpFKenzkgO+3V6H4Pp4lDeCUUkqNKh6fh/fK3wuMZztRfwKA6UnTWZq9lCXZS5iRNGNYdRmsbfZwpMwam7a/pJK95YcpqD9Ci6MIR0QJTncJ4uy4Qp/oSmdSQi5zU2cwK3k6U5OmkhGdMaxe07k6XnOc/9z5n2wu2kxmTCb/Ov9fuXr81Wf12mqbPewqsDN0eZ0Lo+SmxLBwQke3y8yEITwx7M0vz4Pawu7LI+Jg4ef7FxT1FgT5uk/zMKDECc5wcLrsn3BwuILuu+z74V3u97R+53XM5p/R29HQ7ojgYPhMNrZO45WWaXxgJuBwhjEtLc4u3W8Fa7kpMec8D6ExBm9lZY9dHtsKC6G94711JibaXR5zrGDN3+UxOxtH+DC8mBBkqLNyYGVJ9xTX8vK+UjbsK+WYXShmdlY8y+2KlpNTYgb3b11v38f4bPjGB4O337OgAZxSSqkRr66tjreK32Jj4UbeLH6T+rZ6XA4XF6ZfyNIsK2hLi07r304GoEtNi8fL0XJr4uvDZR1FRUqCJr6OiQizSvTb3R6npMUyJSUGj1R3G1t3ou5EoJhDrCuW3MTcQPfLaUnTmJQwCXfYwI7XGSxVLVX89r3fBibi/tLsL3Hb9NsGZHqGFo+XPUW1gakLdgYVRslMiGRhTmIgqBv0k0O/5moo2AYFb8G2X/e+Xq9Bjf9+WA9BUDg4w3pevy/r9LqP0z0nrPP2HQM7FtHrM5xqaKWktoWU/55PBt2nEjhlYnneewlLwg8y0ZdvPc8VCzmX4Jy0FCZcBikzrBr/feBraaGtoKAjOMvLo9UO1nz19YH1JDyc8PHjAhm08AkTiJhgdXt0JiQMwKsPrUBW7u9PWVm51tZBz8r5HS1vCGTm3iusAWDC2GiWzUhl2cw05mUn9DtrCkBLLZx4B05sgy0P9rKSwJqa/u9rAGgAp5RSakQqrC9kU6FV6n9n2U7aTTtJ7iQWZy7m8uzLWZSxqNu8Tedsz1O0P/tVwoKKmLQ73YTd+EiPQVy710d+ZZMVpJXWBwK2/MrOE19PSokJ6v4Yw9S0ODLi3X0OIJo8TRypORIYW3ew6iCHqw/T3G51/XGIg5y4nMC0Bv6iKcmRoeui1VXXibg/OuWj3DX3rkGdiNvrMxwoqQuaj66aUw1Whb7EKBfzxydxwQSry+V5mfEDUxilqco6Ocx/C/LfhNK9gAF/992epqoYRlf8B1Nru5fyOis4K61robS2mZLaFsrqWqxltS2U17cGqkDe4NjCT1x/IEraAttoMuF82/MF/uOBHxAdEQYNFZC/GfI2w/FNUJ1nrRidDDmLYeISmHAZJn487aWltPrHo9ldHtvy8/GUlHQuIJKW1pFFCxqj5kpP77WAyGjjra21snJPPTVkWTm/sroWXtlfxsv7Stl2rJJ2n2FsTARXz0hl+cxUFk0a0/eu4/WlULDV+k4WbIOyDwBjXYgQh5Wp7moYfR81gFNKKTUieH1e9p7aG6gaebTmKACT4idZVSOzlzJr7KyBLSlvDFQcou33ywj31HZ7uCViLJV37uJwRSsH/Vm10nqOVnRMfO0ITHwdVKI/LZacMVHn3IXrdHzGR1F9UedsXdVhTjaeDKyTGJHYKaCbmjiVifETcTmHrmhA14m4L8u6jHvm38PEhIlD1obgthRUNgW6XG7PryLfLowS6QoqjDIhiXnjEogK78M0EE1V1gli/hYo2AKl9glimBuyFlpBRM4lkLkADjw37MfcnKvG1vZAENZbcFbZ2P1kOSrcSXq8m7R4N2lxkaTHu0mNd5Me5+bb6/ayqPE17vU8he9tg+Mi4ReuW9kZdzVvfeuKHtvhLTpA27bnaNuzldYjh2irbKGtPoy2BhcmqDepIzq6o3hIcKXH8eNxROlk1H6ny8rFr1hxTsVWzkZts4eNh8rZsL+MjQfLaWzzEhMRxtKpySybmcblU5M75uEzBqqOBwVsWzsCeleU9X0cfzGMu8j6/eD6Yf991ABOKaXUsNXkaWJbyTY2Fm5kc9FmqlqqcIqTBakLrCIkWUvJjsseuB16PVDyfsc/+hNvQ3P3+d+CtZow9pkc3vNNJt89ncaUeYzJzGVqWhxT06yJr4dDaey6trrAfHX+SphHa44GJikPc4QxMX5iR1BnB3aJ7sQBb0vwRNy5ibncu+BeLs4YXhNxl9e1sD2/OtDt8kBQYZSZmfFckGNl6BbkJJEUHQ6NlVZ3yIK3rKCtzL5SH+aG7AvsgO1SyJwPYT0UzRnmVe+6MsZQ0+TpEow1UxoUmJXWtlDf2n2sXWKUi7T4SNLiIkiLt4KztDgrWPMHarERYb1miZ/ZXcy31+7lc9uf4tr8t1mfs4gnFn6UH98wnY+M8XUUD8nvmODaW1nZsQGnk/D0VMLHRBAe1Ug4hYRH1hMe205Y1iRk4lKru2XOpRA1eJng0aK3rFzix27FPWPGoO+/xeNl2zGrCMor+8uoamhhlvMEH00u5LKII2TWv4+zqcJaOTIJxi2C8Ytg3MWQPtvq9tvVMP8+agCnlFJqWCltLGVz0WY2Fm7knZJ3aPO1EeuK5dKsS7k8+3IuybyEuPC4gdlZWyMUbbe60JzYCkU7wGPPm5Y0EcZdjBl3EWXP3k+aVHd7eqWJpXTizYxr2kdM5QeI3XWRqLHWldys+VaGJfN8cPdvnrfB0O5r50TdCQ5VH+Jg1cFAtq6iuSKwTkpkSrds3fi48eeU6SxpKOFXu37Fi3kvMsY9hq/O+yo3Tb5pREzEXdfiYWdBNTvyq9ieV01B0Qnm+fZzoeOAPeaqAABfmBvHuItg/KV2wHZ+zwHbMBY83qzUDsxK6looq20J6ubYQqudZfYTgZTYiKBgLNLOoAUFZ3Hufl3Q8NbX4zlZwlv/3ErKo78gzHjxidCenEZ4VUXnAiJJSYEy/BHBZfmzspDgAiI+r9WlNW8z5G2y/h54GgGxTvAnLLF+xl0EETHn3PbRLmRZOU8LFO+EE1sxBdvwnXgHp6cBgCIzlu2+aZQlzCVx+lIWLriQiSkD9P8jhDSAU0opFVLGGA5UHWBT4SbeKHyDA1UHAMiOzWZJ1hIuz76ceanzcDkGoHtfYyUUvt2RYSt5367GJ5B2nnVFdvwiGLcIb3QqL+8r5dFNx8g5ub7HMTc/c93Nmu98z1rgbYfy/VZAWLzTuj112F5bIHmqFcxl2T/J062CEMNQVUtVtznrjtccp93uaxbhjGBywuTAJOT+4C42PDawjeB581KjUpmeNJ2tJVsB+PSMT/P5WZ8n2jW43awGVENFR3at4C3rswY8DjeHwmfwWvMUNrdNZY+ZRHK8VenS3+1ycnLMwBRaGAD+8WalwVmz2lZK6+yujbUtlAWNN/MLdzpIjfcHZ5GBYKyjm6Ob5NiIfo0XND4f7RUVeE6exHPyJO0lJfbv9m1JSafiIcFcWVnErVjRaYyaM/4cL5q0t8HJXdbYubzNUPSuNSbKEWZdmJlwmRXQZS0YccH5UBnUrFxzDRS+a110K9hmfVb+MWvJ0wPZNTPuIg63JLBhXykb9pext9jqBp+bEsOymaksm5HG7Kz4YTMe+GxoAKeUGj2GeZcH1aHV28q7Je8GxrOVNZUhCHOS5wTGs02Mn9j/f6w1JzqyawXb4NQha7kzwurKZgdrZF8QyJC1eLys3VXM7988Tt6pRnLGRLFwQhK+PU/xDf6XDKnkpBnDr/g4l668m5vmZfa+/+Ya6+SiaIf9s72jS6YrGjLmWVm6rIVWcBeX3r/XO4g8Xg/Ha493G1tX3dqRmcyIzmBq0lQcONhcvBmPPWeX39zkufzssp+RHjN8X2dAQ3lHwJa/BSrsiZBd0TDuQiu7lrMY0udCWDhen+FgaZ09hs6awqCi3uqemhDlYsH4pEC1y1k9FEZ5ZncxP3/5ECdrmslIiOS+5VNPf2z1wD/erGuXxlI7c1ZW18KphrMbb5ZmL0+KCu93EOprbsZTUmoHY3aQFhScecrKwNP5mHHEx+NKT8eVkRG4dURHU/ajH2GC1pWICCa/+gphycn9amOP2pqsCz/+gK7kPTA+CIu0/ob4A7r0OTACsslDaUCycnUlHX/DT2yDsn0ECo6kz+3oDjnuotN2eS2uaeYVO5h7J68Kr8+QFucOBHMXTkwKfC8H4vs4mDSAU0qNDiNg4s0Pu8rmSjYXbWZT0Sa2ntxKc3szkWGRXJJxCUuyl7A4czFjIsec+w58PitAC64sVldkPRYRB9kXdvyjz5gHrs7l9etaPPz57QIe35LPqYZWZmfF8+Ulk1g+Mw2nQwbmH7ox1uD5IjtDV7wDSvYEJicmLtO6qu/P1KXPhfDhWzjBGENFc0Wg+qU/sMurzetx/fTodDbcsmGIW9lH9WVWsZF8O2jzB/vhMdaJ4fhLrIAtY27PY2a6MMZwoqqJd+2iKNvzq8k7Zc1p5XY5mJfdMXVBcXUTa57fT7PHG3h+pMvJj1fN4qZ5mYHxZsHBWHBBEH+BEP8k1cH6O96sr4wxeKur8RQHBWfBGbSSErxVXcaTOhyEpaZ2Cs5cGR3BWlh6Ro8l6kvWfI+ap5/uHOy5XCTccgvpD3y3X6+jT5prrODeX+Gywuo1gDveOkYmXGb9JE/r85QFHwZ9ysoZA5XHggK2rVCdbz3miobshR09JTIXnPPfx5qmNl47UM6G/aVsOlxBi8dHnDuMK6enkhAZxt+2F9Li6egiHPx9HA40gFNKjVzGWFfF87fAhvuhvbn7Oq5ouPzfrO5ryVMhLmvA5ydSPTPGcKzmGBuLNrKxcCN7KvZgMKRGpQaybAvTFhLhPMcuSF4PnHzP+gd/4m0raGu2s0ExqfZA9Yut29SZvV4ZL6tr4fEtefzlnRM0tLazOHcsdy2ZxKJJY4ama42nxRp/U2xn6Ip2QI01ngpxWm3PWtCRpRszedgfw7OfnB2Yny6YIOz5zJ4QtKgH9aUd2bWCtzq6u4bHWMdMjj2GLX1OnwK2viivb2FHfnUgqDtQUofvNKda4U4HGQluSkI03iyYaWvDU1YW1J3RDtCK7exZSQmmpaXTcyQqygrI0jM6grRM+zY9nbDUVCTs7LsRH79pJa0HD3ZbHjFtGhOfWXfOr/Gc1ZdZU0Pk2Rk6f8ARnWIFcvaUBSTmDH3bhqEes3I5KSTMjiQ+4SiONnsMbtQY67voLzqS1kvBkX5qbvOy5egpXt5XymsHyqhu8vS4XmZCZK9VToeaBnBKqZHD5+sI2PxXypu6T+Z6Wq4oGDvFujKabN+OnWr9Yx2m45FGEo/Pw66yXWwstIK2ogYrAzZjzAwraMtayrSkaecWGLU2WAGOvwx00Y6OoD1pUkd2bfwiSJxwxivfxyoa+N2m46zbXUy7z8eK2Rl86bKJnJc5DIqNNFTYAd0O67Z4F7TWWY+5463un5n+oG4+RPcjczkIlv1jGSWNJd2WhzQDV1did4l80/obUmlNQ0F4rHXM+AO2tDlD9reg3i6M8tkntve6zvVzMgZlvFlX3ro6KxArDgrOgsaftVdUdJoTDcCZPNYKzoK7OGYGdXWMH5nji/qtuqCjIEreZmgos5YnjO/objnhMohNDW07Q8HTbI0RtrNr3qPbqT3io+ZYFK21LhwRTuIunUXibZ/GffE1Q57BbPf6yP33l3q4/AQC5P1kxZC2pzcawCmlhi+fz+qakr/FOukq2ApNdinouKyOE66cS+DJG1jfXslDiQmUhjlJa/eyurqGFWFj4M6NUHHI6hJVEfRT3zEvFs5wK7ORPNUK6PwZuzGTdZD6GdS21vJW8VtsLNzIluIt1HvqCXeEc1HGRSzJWsKSrCWkRp/DiUrjqY7MWsFWq+CI8VqTrKae15FdG7forE6Edp+o5tFNx9iwv4xwp4OPLsjii4snMn7MMC6o4fNZGaJAlm4nlO+zxuGAFbBmLezofpk2C8LCT7/NQbT++HrWbF2Du7aZrz/j5Zc3OWmNj2TNxWtYMXGIToDqTnZk2PK3QNUxa3lEnHXsjL/EDthmh/zizSU/eZ3imu49CAbqir/xeoOKg5QI1qshAAAgAElEQVT0OP7M19DQ6TnichHWNXsW1MUxLC0NR4T+bTwjey7JQECX/ya02HNKJk/rCOZyLoXIhNC2dTA0V1sFR/xd24t3dXQZT5kR6Clhsi+iOa+i+1i5j91K/LXXDvq8csEG+/s4EDSAU0oNHz6fVdktOGDzF3yIzw4K2C61rmQGXZlbv/F+1uStoyVokL3bZ1gzYSUrlv6g5/211FknxRWHrMzeqcPWbXUB+K+/icM6Oe6UsZti/XyIy0kX1hXyRuEbbCraxM6ynXiNlyR3EkuylrA0eykXpV9ElOssxiYYYxUc8QdrJ7Z1dGlzRliBybjggiNnVwbaGMPGwxU8uvEY7+RVEecO49OLcvjsJTmMjRmhJ6GtDVYxBX9xlOKdUG9nvZwRVvnz4KqXXb4zg2398fWc/O4DXLyjkbfnR5P+ne/wkdzrwOkcnKxMbZE9fu1NK9NWddxaHhFvBWz+iz1ps4ddoQn/vGbu+mq+tf3P/GTh7bTEJvZ5zI2vqQlPaWn38WfFQcVB2juPj3PGxxOW0XNw5kpPxzlmDDLMu+qOSD4vlO7pKIhyYps1dYk4rO66/oBu3EUQPowvKvWm7mTnccjl+7EKjrissceBwlEX9lpwpNtYueho4q6/jsRbh2ZeOf/3sbcxqcOBBnBKqdDx+azJboNLc/vHMCWMswaD+6+SJ47H6/NS0VxBSWMJJxtOUtJYQmljKScbTrLt5LZAifNgDnGQE5dDjCuGaFc0MeH2rX3f/xPjiul4TMKIqq8gpu4kMVV5uE4dtYK8qmN2yXlb/LjOQZ0/yIsc+ImPQ83r87Ln1J5A18jjtdbJ8eSEyYHxbLPGzsIhfTzh83eHDa4sVldsPRYRb1X5849hy5h3zlnQdq+PF/aU8OimYxwsrSctzs0XFk/g4xeMIyZiFHaZrS3uKI5StMMaI+jvZhqdbAd0dtXLjPPPOhDuC+Px0Pj2O9SsW0v9iy91X0EECQtDXC5wuRD/j72s6y0u//0u65lWpLkCaSqDhmKkrRZxGCTcjYzJQZInISm5yJjx4AoP2o9/n132499uL+0hrP+FPk7nmd3FnFzzPZYc2sLGqZeSueaBQAETb1VVl3L6nTNo3pqazhtzOglLTbGDsZ6yZ+k9FgdRIdDeZn1fA1MWbLcyVA6XdbHK3+Uyc35Is+o9MsbqihwI2LZ2jN91RVvt9/eUyJx/1gVHeq1gOQRZOa1COYA0gFNqhPN5rYAt3x6/VvAWtNgnHgnjaRp/MaXpMzmZmEmJeClpKKGk0f5pKKGsqQyv8XbaZHxEPOnR6Rys6j6g3e/q8VfT0NZAY3sjjW2NNHgaaPQ00uhp7LHQQlfhjnAruAuLIsbhIsoYYtrbifY0E9NST3RTNTHtHqKNj2ifISY8jui4TGISJhA9JpeY5OlEp8wiMmEcjmF25f90mjxNbD25lTcK3+DNojepbq0mTMKYnzafy7MvZ0nWErJis/q2sfY2qwukP2ArfDuo4Eha5/FrKTP6nSFpbvPy1I5Cfrf5OMU1zUxOieFLl03kxrmZhId9iLIKXk/H3HRFO60Txa5z0wWqXi6ElOnn9N6b/8/efYdXVWV9HP+eW9ML6YWWAKErVYr0bi9gwSnOjDpFFGxjVxR7l9c+tnGEEUF0bAQQC9Jb6BAgIZBKek9u3e8fJ6SQgJRUsj7Pw5Pk3HNO9iUY7+/uvddyOCjbuIni+GWUrvwBV1ERmEzgcukv8oxGPPr2xWfMaL30u9OJsjtQTifKUfujQ//ocIDjhMds5ajyYlRlGcpWAU4Xyg1KGfQ/bjhlRZDGcKZh8wwCqdtuI/+jj8DpAqMBzwEDcOXm6cVBbLY6w6guDnI8oJ1YvTE09KyKg4hWwF6mh6HjFS4zdwBKD0S1WxaE92v+mWSXU589PFr1ptvRDVB2vOBIcM3sWqfhjb40uWZWbhG2g4eafVautZEAJ4RoOm6XXl0vZQ3q8K/kpW8g01lOpslIpl8omf4RZHr6kImLzMo8Cm1130U2akZCvUKJ8I4gwidC/1j1J9InknDv8OpGwP0/GI0yFdQbguYMZOdfVjc8POWmwllBmaMq1NUKd7VDXkOPlTvKa762l2J31++tVG8sSuGNAW+DBR+zF94WP3w8g/D27IC3xadmltDsg7el7ixhncfM3pgboRJX7UbL4d7hzB44m0Fhg/RZtrSf2ZS5CYfbgZ/Fj1HRoxgbPZaRUSPrNGs+KVup3vz2+Oxa7YIjQd3qVogM7NJoS/sKyux8sv4I/16fQn6ZnUGdA/nbmFgm9AxtNY2UW1xFgb4P5Xiz8bQtDfSmG1wT7E7Sm045nZRv2kTxsnhKVq7EVViIwcsLnwkT8B4xnKy5T9QJHmfcp6vgSK0qkWv0Jbagz3AfL+nfZSSE9qmuyqncbj3w2R0oh10Pio4TgqK9JijWe9zRQJCs91jdc07+uLPWPRwnCazO6nucyBgUhNeQIQ0XB/Hza5/FQdqjigL9v4HDq/U/x/sRegRA11E1Sy6DezT+EmlHhf77obpw1GawV+2VDOhc8zu88wj993oz/JtUSlGRsJ3Cz2vNyvXrp/eVa+a9ci1JApwQolHYXXaOlWaQkbqWzLT1ZObsJbMknQyDmyyTkUyTGfsJv9s9TZ5EekdWh7PjoSzSO5II7whCvEIwGU79Dl5eqY2vtmfw7OoFeEQsRTPUlP9VbjOVmdfw09/upmOHpu2lZXfZqwNfTegrpbQ4jdKCZMqL0ygtyaCsIpfSygLK3A5KDRplmoEyo5FSk4UyTaMM12nMCdaaFTwx3Fm88TZ51wmAPmY9HHqZvarP2ZCxgVe3vkqlq6bst4ZWPSPZybdT9dLIAaEDfvPnoBccWV/TtydzZ03BkfB+NbNrnYaDT+g5/E03LK2gnPd/PcyizalUOFxM6BnK38bGMqTLyZu6iipK6fvF0rfW7KfL2lWrN120vuwyajAqYiDlaU6Kf/hJD20FBWheXviOG4fftKl4X3wxBg+PM+/TpZS+9Kp20ZGiVP0xzw56UOsySl9OHdKr1bdROFOOY9kkTZ58boFXtA8lWXD4Vzj8MySvhqKqNzZ8wuu2LAjoVPe6nZ/Dqif1vaL+0TDhsfo9UisK4OjGmpUSGQlVvwc0fWXE8d/hnUeAX2RzPNtTas+zchLghBC/SSlFsb24eiljRlmGvvesJJ2swiQyyjLJddWv2BSsWYj0DCE8MIbIgNiacFYV2PwsZ/cussPl5pfEHBZvTWXVvmycboXZqKG8t2ENWY5mLkQ5ArDlTMFZPACAvlF+TOsbwdS+4cSGtILiI2W5DVbGdJdkUKFplBkMeqgL7ExpQBRlfuGUegVR5hVAmcWTUldlveWgtWcOT3dW8ES+Fl8+veRTuvp1PfnP5viL7eOza7ULjpg89FmbzrUKjlhPY8buLCVmlfDuL0n8b0cGGnDFhZH8dXQsceFN9z3bheO96dI2o1I3U755E8X7SihJ9cBlM6KZwbdPBL7jL8bnkhkYIvvUCVW/2afreEPz48upU9bUNF33CtKDWueqgkUhPc+7wHaiFm9MLdqu/MM1s3OHV0NZtn48sGtNoCsvhJUP6zNqx5k9YcITeguS43vYsvfqjxnMEDWwJqx1HNqq93a3x1k5CXBCCJxuJznlVcVBjoezE4qElDvL61xjQSPC6STc4SDS6STC7E9EUA8iIocQ0WUc4WH9sRgbd8P1gWMlLN6SypcJGeSW2gj2sXD1gCimD+rIvsziBqtG3Tu5By6l+H5XFttT9SWaPcJ8mNo3gml9w+kZ7tu6liJVFkHuwd+ojGmEDl3rF08J7lGnapnD5agb6mqFu/t/vb/Bb99go+Xj7RxqVxY73oLBwx86DqvZwxZ5YZO3XVBKsTlFbwXw4/5svCxGbhjSib+M6kpUgGeTfu/2QrlclG/ZSnH8MkpWrMSVl4fm4YHv4J749vTBJyATQ/Z2sJfoFxzvTXe82XhxGix/qO4LRpMH9JsBTpu+//V40Rqv4LoVZkN6Nnvvp5bW6hpTi7ZJKf3/F8cLoqSsAVvRb19n8dFD2vGVElGD9IDXBrWXWTkJcEK0A+WO8vqVG8syqouEZJdn1ysOEmANIMIrnAiTFxH2SiKKc4jITSaisowIp5MOHWIxHF/S1Hkk+IY3ydiLyh18vTODJVtS2ZFWhMmgMb5nKDMGd2RsXEidRra/VTUqs6iC+N1ZLNudxeaUfJSCLkFe1WGuf3QrbjrrqNCD3fFAl7Mfcg6cpDJmrT52wXENVsY8ZaPlq77Vy9MfD2xHN9QUm/GNrLtRPbR3s82OuN2KH/Yd451fkth2tJAO3hZuHtGF3w/rTKB3K6vO1gYpl4vyrVspiV9O8YoVuHJz0Tw88Bk7Fr+pU/EZMxqDZ60XdW6X/u+xdhuD7L01velOxju0aknkxfqyyKbYuyOE0P8bzdwO/zpF77Lbfoawfi3eC7Gxne+zchLghGjFGioycWITXLdyk1+ZX2/GrLp6Y1kmRSe8A2fUjIR5hdUtDOITQYRnCJFlhYQfS8Tr6Eb9hbujTL8opGdNSf/OI8+ocfKZcrkVaw/lsnhrGsv3ZGF3uukZ7sv0QdFcNSCqUfp25ZTYWLE3i/jdWaxLysPlVkQFeDK1bzjT+oYzsFNg2yh64XLo+5eOB7qc/fqyzNyD4KzZ34ZPWJ0m5d9lbWBu3sYT+ua5mVtp4dK8jJprg7rXrRDZzL3EAOxON19tT+e91ckcyi4lOtCT20bHMGNQRzwtbaeqZ2uk3G4qtm2jeFk8xSuW48qpCm1jxuA3bSo+o0dj8DqD/aO2Un3fzL8vO8kJGjxeIIFNiOb0at+aPaW1+XeEu3Y3/3ia2Uln5a6/Ho9evVp6eGdFApwQrdR3yd8xd93cOkUmzAYzo6NG42v1rZ49yyrLqrfXydvsXadiY+0iIRHeEYR4hmA0GPXS7hkJNY1vj26sFdh61TS+7TyySQpPnOhwbhlfbE3ji21pZBZV4u9p5qoLI5k+qCN9o5qu6lphuZ2Ve48RvzuLXw/mYne5CfW1MqWPHuaGdu2AydjG9uC4XXrVvgb22R1f9vadtxevBwaQZTIS7nQxu6CQSyscMPTWmhk2n5YroFBqc/LfjUf5YM1hsoor6RXhx9/GxHBpv4i29/NoRZTbTUVCAsXxyylZvhxndjaa1YrP6NF6aBsz5tzfnW7nLxiFaFV2fg7f3Fl/D9zl8+sXMjmPnWxWLvD66/CbNq1NzcpJgBOilTrZEjeAEM+QmlDmXVW5sSqcRfhE4Gs+yb4upx0ytumBLWUtpG4ER9XettDeNXtQOo8E7+AmfHY1Sm1Ovt+ZyeKtqWxOKcCgwegeIcwY1JGJvUOxmpp3hqWk0sGP+7OJ353FT4nZVDrcdPC2MKlXGFP7hTMyNrht9xFTCkoy4ZXe0GC9Sw3mFjZwvPnklNj4eN1h/rP+CMWVTobHBPHXMTGM6RHSepe4tnLK7aZi+w59T9vyFTiPHUOzWPAZMxrfqVPxGTO2cRs7ywtGIVqX06lC2Y6czqycIzub9LvvIfrVV1pdNVgJcEK0QjtydvC773/X4GMNFpk4GadN7/eUskYPbambanpxhfWtuyTSO6iRRv/blFJsPJzP4i1pLNudSbndRUywN9MHR3PNgGjC/T2abSynUmF38cuBbJbtzmLVvmxKbU58PUxM7BXG1L7hjOkRgoe5jS7ha4UzJEfyynhvdTKLt6bhcLmZ0jucv42N5cKOAS0ynrZOud1U7Nih72lbvhxnVhaa2Yz36NH6nrZx4xo3tJ1IXjAKIVq5U83KlW/fQdEXXxBwww2trhqsBDghWpEiWxGvbXuNLw58gaZpuBsoBhDhHcGK6SsavoHTphcUOLK2VmCrBDQ9sNVeEunV/P2x0grKWbotnSVb0ziaX46P1cRl/SOYMTiagZ0CW/Xsis3pYs3BXJbtzmLl3mMUVTjwshgZFxfK1L7hjOsZio+1DW0Cb0UzJLvTi3j7lySW7crEZDBwzcAobh0d0zraPbQxSikqd+7U97QtX44zM1MPbaNG6csjx43D6CN/r0IIcaITZ+WOa409Gc85wGmaNhV4HTAC7yulnjvh8b8BtwMuoBS4TSm1t+qxB4G/VD12p1Jq+am+lwQ4cb5SSvFt8re8tOUlimxF3NTrJmL8Y3huw9NUqpq+QB6ambkXz6spZOKohPQtNY1v0zbXBLbwvjWNbzsNb5HABlDpcBG/O4vFW1NZl5SHUjAiNogZg6OZ2ieiTRahcLjcbEjOY9nuLFbsySK31I7FZGB09xCm9Q1nYq8w/L3MLT3M39aCMyRKKdYl5fH2z0msOZSLr9XEzGGd+MvIroT6tY4Z2LZCKUXlrl1VoS0eZ0YmmM34jByph7bx4zH6Sl88IYQ4HUop0u64k9JVq/RtB62wJ+M5BThN04zAAWASkAZsBm48HtCqzvFTShVXfX4F8A+l1FRN03oD/wWGApHAD0APpU6oZV6LBDhxPkouSuapDU+xOWsz/YP78+jwR+nZoSfs/JzvfriP1/28aopMFJdz6QV/0ZtsHg9sLhugQUT/msa3nYe3aNNNpRQJqYUs3pLGtzsyKLE5iQ70ZPqgaK4dGE3HDmdQ1a6Vc7kVW1LyWbY7i+V7ssgsqsRk0BjRLZhpfcOZ3DuMoEaomnm+cLkVy3Zn8u4vyexKLyLE18qfR3blpmGd8PNoA6G3lVBKUbl7j76nbVk8jowMPbSNGIHvtKn4jh+P0c+vpYcphBBtjiM7m6RJk1E2W/Wx1jYLd64BbjgwVyk1perrBwGUUs+e5PwbgT8opaadeK6macur7rX+ZN9PApw4n1Q6K3lv53t8tOcjPE2ezBk4h+k9pmPQqgpknGyPEoBmgPD+NUVHOg0Hz5bfJ5RdXMnShHQWb0klKacMD7OBS/pGMH1wNMO6BrWNsvznwO1W7EgrrO41dzS/HIMGF3UNYlq/cKb0CSesnc4uVTpcLNmaxr9+TeZIXjldg725bXQMVw+Iarv7CJuZUorKPXspiV9GcfxyHGlpYDLhPXIEflOm4jthPEZ//5YephBCtGmZc5+g8IsvwFGzAqq1zcKdKsCdzmaOKKD2K8w04KIGvsntwN2ABTjeTTAK2HDCtVEnXIqmabcBtwF06tTpNIYkROu3Jn0NT294mrTSNC6PuZy7B99NsGdV1UenHQ7/cvLwhgb3p4BH63ihZnO6WLUvmyVb0/jlQA4ut2JQ50CeuyaGS/tH4NuOZlUMBo0BnQIZ0CmQB6b1ZG9mcXWYe+x/e3jsf3sY1DmQaX31MHc+zUSeTFGFg083HOGjtYfJLbVzQbQ/D9w0kMl9wjGe54G+MSilqNy7Vy9EEh+PIzVVD23DhxP897/roS2g5d+8EUKI80XF9u11wxuAw0FFQkLLDOgMnc4M3HRgqlLqlqqvfw9cpJSadZLzZwJTlFJ/1DTtDWCDUurTqsc+AJYppZac7PvJDJxo646VHeOFzS+w4sgKuvh14ZFhj3BRxEV6IYlDq2Df15AYD7YiQKPBMu+tpI/S7vQilmxN43/b0ykodxDu58E1A6OYPiiaGCk+Uc+h7BKW7dLD3N7MYgD6RflXNw4/3/7Osooq+XDtYRZuPEqpzcnoHiH8bUwMw2OCWnWxmtZAKYVt/359T1t8PI6jR8FoxHv4cPymTcV3wgQJbUII0Y6d6wxcOtCx1tfRVcdO5jPg7bO8Vog2y+l28tn+z3hj+xs4XA5mXTiLP3WfjiX5Z/j1LTi4Um+g7RkIvS6H3ldAeR58d3f9KoETWm76Pr/MzlcJ6Szemsa+zGIsRgOT+oQxY1A0o7qHyIzKKXQL9eWOCb7cMaE7R/LKqmfmXlyeyIvLE4kL89XDXL9w4sJO0sevDTiUXcp7q5P4MiEdl1txWf9I/jomhj6RrWPGuLVSSmFLTKQ4Pp6SZfHYjxzRQ9tFFxF06y34TpyIKbDl9rUKIYRoG05nBs6EXsRkAnr42gzMVErtqXVOd6XUwarPLwceV0oN1jStD7CQmiImq4DuUsREnG925exi3oZ57Mvfx8jwoTwcMIiOyb/qM24uG3iHQq/LoNcV+n42Y60lh62gj5LT5eaXAzks3pLGqv3HcLgU/aL8mTE4misuiCTAy9Ks4znfZBRWEL87i/jdWWw+ko9S0DXYu3pmrl+Uf5OGucZqVLrtaAHv/JzEyn3HsBgNXD+kI7eOimkXy0TPllIK24GD1YVI7CkpYDDgPewifKdOxXfSJAltQggh6mmMNgKXAK+htxH4UCn1tKZpTwJblFJfa5r2OjARcAAFwKzjAU/TtIeBPwNOYI5SatmpvpcEONGWFNuLmb9tPp8nfk6wyYv7Xb5MTtmG5naCX3TNTFvHi8DQ+oo4HDxWwuKtaSzdlk5uqY0gbwtXD4hi+uBoeoZLdbumkF1SyYo9x4jfncX65DxcbkVUgGd1mBvYKbDRC8Fkzn2CwkWLzqpRqVKKnxNzePuXJDYdzsff08wfh3fmjyO6SOXNU7AdPFi9PNKenAwGA15Dh+I3dSq+kyZiCgpq6SEKIYRoxaSRtxCNTCnF93sX8mLCfAqc5dxYUsKs/EJ8Arroga3XlRA1EFrhErmiCgff7Mhg8dY0dqQWYjJojOsZyoxB0YzrGYrZaGjpIbYbBWV2Vu7Tw9yag7nYXW5Cfa1M6aOHuaFdO2A6y5+Hcjqxp6ZSkZBA5qOPgcsFJhNhDz6IpWM0Rj8/DH5+1R8NlrqzrA6Xm293ZvDuL8nszyoh0t+Dv4yK4YYhHfFuS83Mm5Ht0KGa0JaUpIe2IUP0PW2TJkloE0IIcdokwAnRWAqOkLLjE546/BUbDXb62Gw85vKnd9xVenAL69sqQ5vLrViXlMviLWks35OFzekmLsyXGYOjuWpAFMEyk9Liiisd/LQ/m2W7svj5QDaVDjcdvC1M7h3G1L7hjIgNxmKqH+bcdjv2wynYk5OwHUrClpyE/VAS9pQU1IkVtk5B8/DA6OeH5utLnmblcKWBXCyY/P3p3SOSXt2jMAcEYPTzrQ5+evjzx+Dt1Wb3850rW1KSvqctPh7bwUOgaXgNGYLv1Cn4TZrUavoJCSGEaFskwAlxLnIPwb7/Ydv7Pz6oSOH9AD+saMwOGsqMi+7BGNq7pUd4Uim5ZSzZmsbSbWlkFFXi72nmygsjmTGoI32j/Nrti+7Wrtzu5JfEHJbtzuLH/dmU2pwEG11cE+RgtLWMmPJsXCkp2A8dwp6aCm63fqGmYY6OxhobiyU2BlNIKDkvv1wnyGkWC1Gvv4ZmNOIqKsZVUoy7uJiy3AISkzLITMvGUlFGqOYgVLNjqSzHXVICp/p/hdGI0dcXg78fRl+/OrN7Rn8/DL7HA58vBj9/jP5+Vef7Y/T1RTO1rRk9W/Lh6j1ttoMH9dA2aJDeXHvSJMyhoS09RCGEEG3cuVahFKJ9UQqy98Ler/WS/9l7WefpwdNhERz18Gda1GjuGzGXEK/W+c56mc3Jd7syWbIljU0p+Rg0GNU9hIcu7cXEXmHSULmVcxUVQVIyw5IOMfBIMnenHaLkwEFMOceqz8nTDBQHhWOJjSVy8hR84npgjY3B0rUrBo+aJuKZc5+o16RCKUXp6l+r98KlFZTz/q+HWZSeSkVYfyaODeVvY2IZ3KVDzTUuF+7SUlwlJbiKinCXlOjhr7gId3EJruJi3CXFVcf0QOjIzMRVUoK7qOg3ZwINXl7VYc7o51fzee3wd/xz/7oBUfPwaPQ3Ihoq+mI7fJiS+HiK45djS0wETcNz0EDCHn4Y38mTMYdJaBNCCNE8JMAJAXpoy0jQA9veryE/CdDI6XwRL/afwLKSg3T2i+a9ix5meOTwlh5tPUopNh3OZ/HWNL7flUm53UXXYG/umxLHtQOjCff3+O2biGajlMKVm4stKRlb0iHsScnYkvTlj66c3OrzNKsVS0wMHS4agjU2BmPXGPaaO/BdvokVibnkltqxFhgYnR/CtEhfJigjtQv5n6pR6b7MYt79JYlvdmaiAVdeGMVfx8TQI8y33ng1oxGjvz9Gf3+Ijj7j56oqK3EVl+AuLqoJgcXFuIpL6oTA6vCXlkZlSTHuomLcZWWnvL9mNtfay+eL0c//hM+PL/n0r7/809cXzVB/WWruW29TsXUrx55/Hmu37hTHx2Pbvx8Az4EDCXvoIXynTMYcFnZGfxdCCCFEY5AllKL9crshbVPVTNs3UHQUNCN0HY2r12V8bnEzf89H2Fw2bu13K3/u92esxta1Vyy9sIKlW9NYsi2NI3nleFuMXNY/khmDoxnUOVCWSLYwpRTOzEw9nCUlYU86vk8tGXdRUfV5Bh8fLLExWGO76TNpsbFYY2MxR0aiGRueMXW5FZtT8qvbE2QVV2I2aoyIDWZa33Am9wmng7eFrxLSeXF5IhmFFUQGeHD1gCh2ZxTzc2IOXhYjNw7txF8u7kpkgGdz/bWcEeV06jN5dQJfQ+Gv4c9xnbRrDWgaBh+fqlk/ffmnZrVStmZNzbJUwHPAAL0QyeTJmMPDm+FZCyGEaO9kD5wQx7mccGStPtO271sozQKjBWLH6z3a4qaxpyKTeevnsSdvD8MihvHwRQ/Txb9LS4+8WqXDxfI9WSzeksbapFyUguExQcwYHM3UvuF4WWRivbkppxNHWlpVUEvGnnRIn11LTkaVl1efZwwMrNqfFlu9T83arRum0NBzCttut2J7WmFV4/BMUvMrMGgQG+JNSl45Dlfd3/PeFiN/GxPL74d3Pq97/CmlUOXldWb3XFXhz11cVLUHsOrzqsBnO3gQd3GxfgOjEb/LLiPq+eda9okIIYRodyTAifbNaYfDv8De/0Hi91CeByZP6D4Jel8J3SeDhx8l9hLeSHiDzxI/I9AayP1D72dql6mtYhZLKcX21EIWb03jm9V9onEAACAASURBVB0ZlFQ6iQrwZPqgaKYPipZGys3EbbdjT0nRZ9JqLX+0Hz5cZ5+XKSysTlA7Pqtm6tDhFHdvHEop9mQUE787i3d+ScLprv87PtLfg3UPTmjysbQ1juxskiZNRtls1cc0q5VuP6yUapJCCCGalRQxEe2PowKSfqwKbfFgKwKLL8RN1Wfauk0Eix56lFIsT4nnhU0vkFuRy/Vx13PHwDvws7R8I+vskkq+3JbO4q1pHMouxcNsYFrfCGYMimZYTFCjN3wWOnd5Obbkw/VL86em1izJq1Xx0XvUxTXLH2NiMPrW30fWXDRNo2+UP32j/Hnzp0MNnpNZVNnMo2obct96G1Vr6SSAcrvJeevtM26ALoQQQjQVCXDi/GErhYMr9OWRB1aAoww8AqDXZXpoix0Hprp72I4WH+XpjU+zLmMdvTr0Yv74+fQN7tusw667R8mTuyZ2x9tqYsnWNH4+kIPLrRjUOZDnrunHJf0j8PMwN+v4zmeuoiJ9yeMJQc2RkVFzksmEpXNnrD164Dttak1QO6HiY2sUGeBJemFFg8dFfacq+iKEEEK0FrKEUrRtFYVwIF4vRJK0CpyV4B0CPS/TG2t3GQXG+oHH7rLz4e4P+dfOf2E2mrljwB3cEHcDRkPzltj/KiGdB5fuosJRv9BCmJ+VawbqSyRjQ3yadVznE6UUrry8miWPVUVEbEmHGqz4aI2Jwdqt1j61Tp3QzG0zNDf078vTbOTZa/px1YCoFhyZEEIIIU5FllCK80tZLuz/Tp9pS/4F3A7wi4JBN+szbZ2GwSmC2MbMjTy14SlSilOY0mUK/xzyT0K9mr+HU5nNybxv9zYY3oK8Lay9fzwmY/0S56LhPl01FR/rlua3JyXpvdWqGLy9sXSLxefiUXpQi9ELiZyq4mNbdTyk1Z7hvW9KnIQ3IYQQog2TACfahuJM2P+tvqftyFpQbgjsAsP+rhciiRwIDfRzqi23IpeXtrzEd8nfEe0TzdsT3+biqIubZ/yAzeki4Wgh65LyWJ+US8LRwgYLTADkl9klvJ1C9ksvU7F1K6mz7sDapYse1JKTcTdQ8dF36tRGrfjY1lw1IEoCmxBCCHEekQAnWq/Co1U92r6G1E2AguA4GHWPPtMW3g9O44W4W7lZcmAJr219jQpXBX/t/1du6XcLHqam3b/kcit2pxexLimPdUm5bE7Jp9LhxqBBvyh/bh0dw+ebU8krs9e7VvYo1afcbsrWriXvgw8p37ABgModO3Ckp+PRowf+117b7BUfhRBCCCGamwQ40brkHoJ9/9ODW+Z2/VhYPxj3kB7aQnue0e325+9n3vp57MzdydDwoTw87GFi/GOaYOD6Er6D2aWsPZTLuqQ8NiTnUVLpBKBHmA83DOnEiNggLooJwt9T31MVF+bb4B6l+6bENckY2yJXcTFFX35J/sKFOI4cBQ+rPtvqdoPZjO+kSVIhUAghhBDthgQ40bKUguy9NTNt2Xv141GDYOITeiGSDmceuMocZbyR8AYL9y8kwBrAMxc/w2UxlzX60rnU/PLqwLYuKY/cUr1/VKcOXlzaL4IR3YIZHhNEiK+1wetlj9LJVSYmUrBgIUXffIOqqMBz4EA63Hwz2c89X1Pq3eGgaOlSQv7xd+nTJYQQQoh2QQKcaH5KQUaCHtj2fg35SYAGnYbD1Oeg1+XgH32Wt1b8cPQHntv0HDnlOczoMYM7B96Jv9W/UYaeXVzJ+uQ81h3KY21SLmkFeon2EF8rI7sFMTI2mOGxQWfUWFv2KNVQDgclq1ZR8OkCyrdsQbNa8bv8MjrMnIlH795kzn1C+nQJIYQQol2TACca387PYdWTUJSmB7EJj0Hf6ZC2uSa0FR0FzQhdR8Hw2/Wy/75h5/Rt00rSeGbjM/ya/itxgXG8MvYVLgi54JzuWVTuYMPhPNZVzbIdzC4FwM/DxPDYIG4dFcPIbkHEhvi0q8IYjc2Zk0PB4sUUfrYIZ3Y25uhoQv/5TwKuuRpjQED1edKnSwghhBDtnfSBE41r5+fwzZ3gqNU82GACszfYisBogZhx+tLIuEvA69wLTThcDj7e8zHv7nwXo2bk9gtvZ2avmZgMZ/7+RLndyZaUAtYm5bI+KY/d6UW4lb4vbUjXDoyI1WfZekf6YTRIYDsXSikqtm+nYMFCipcvB4cD71GjCLxpJj6jRp13Jf2FEEIIIU6X9IETzWfVk3XDG4DbCS47XPM+9JgMHo2znBFgc9ZmntrwFMlFyUzqPIl/Dvkn4d7hp3293elme2oh65L0GbaEowU4XAqzUWNAx0DunNCdEbHBXNgxAItJyvo3BndlJcXffU/BggVU7t2LwceHDjNvJOCGG7B27drSwxNCCCGEaNUkwInGVZTW8HFnJfSf0WjfJr8yn5e3vMzXSV8T5RPFmxPeZHT06N+8zuVW7M0oZl1SLmuT8th8OJ8KhwtNg76R/vz54q6MiA1mSJdAvCzyn0djsqelU/jZfylcvARXURHW7t0JnzsX/8svw+Dt3dLDE0IIIYRoE+QVqmhcfhFQnFH/+FkWJTmRW7lZenApr259lXJnObf0u4Xb+t+Gp6nhvmlKKZJySll7SO/FtiE5n6IKfQ9V91AfrhsczYhuwQzrGoS/l7lRxihqKLebsvXrKViwkNKffgKDAd+JEwm8aSZeQ4bIvkEhhBBCiDMkAU40ruCe9QOc2VMvZHKOEvMTmbdhHjtydjAobBCPDnuU2IDYeuelFZSzriqwrUvKI7tEL+0fFeDJlD5hjKwq7R/q17SNvNszV2kpRV9+RcHChdgPH8YYFETQ3/5K4PXXYw4//SWuQgghhBCiLglwovHkHoKU1dB1DOQn161C2f+6s75tuaOct3e8zX/2/gc/ix9PjXyKK2KvqJ69ySmxsT45j/VJuaw9lMfR/HIAgn2sjIgN0guPdAs+o9L+4uzYDh2iYOFCir76H+7ycjwvuIDIF1/Ad8oUDBZLSw9PCCGEEKLNkwAnGs+quWDygGvfB5/Qc76dUoofU3/kuU3PkVWWxbXdr2XOwDloypsf9mWz9pBeKTLxWAkAvh4mhsUE8aeRXRjZLZjuoVLavzkop5OSn36iYMFCyjdsQLNY8Lv0UgJnzsSzX9+WHp4QQgghxHlFApxoHEfWw75vYNwjjRLeMkozeHbjs/yc9jPd/Ltzb9+HyMqJ4I8f7GFXWiFuBR5mA0O6dODKAZGMjA2mT6QfJqNUimwuzrw8ChcvoWDRIpyZmZgiIwi5524Cpk/HFBjY0sMTQgghhDgvSYAT504pWPEw+EboTbnPgcPt4OPdn/DOjndwK0WoYzp7Ng8iYUM5JkMyF3YMYNb47oyIDWJApwCsJukV1twqdu6kYMECir9fhnI48B4xnPBHHsZn7Fjp3SaEEEII0cQkwIlzt2cppG+FK98Cy5nvM3O7FXszi1myZzXfZbxBpZaBo6Q39mNXEBXaiZtHBjM8NoihXTrgbZV/si3BbbNRvGwZBQsWUrlrFwYvLwKuu47AmTdija1fSEYIIYQQQjQNeTUszo3TBj88AWH94IIbTusSpRTJuWWsO6RXiVyXcoRK32+wBGzB4A7kIt97mD5oChd1DSLQWwpftCRHRgYFny2icPFiXAUFWGJiCHv0EfyvvBKjj09LD08IIYQQot2RACfOzaZ/QeER+P2XYNCXz32VkM6LyxPJKKwgMsCT+6bEMbRrh+qiI+uS8sgqrgTchITvwtjxGzyoYHr3P3DPkNvxMku1yJaklKJ840YKFiygZNWPAPiMH0eHm27Ca9gwKQwjhBBCCNGCJMCJs1eeD6tfgG4TIXY8oIe3B5fuosLhAiC9sIK7Fm1HVV0S5G1heGwQ3aNLWVf0HnvzdzAgdACPDHuEHoE9WuiJCABXaRlFX/+PggULsSclYQwMJOiWWwi84XrMkZEtPTwhhBBCCIEEOHEuVr8EthKYNK/60IvLE6vD23EK8Pc0seivw4kONPKv3e/x8Z5P8LZ48+SIJ7my25UYNKke2VJsyckULPwvRV9+ibusDI++fYl47ln8pk3DYLW29PCEEEIIIUQtEuDE2ck/DJvegwG/g7De1YczCisaPL24wskxRwKzv3mGjLIMrup2FXcPuptADyk33xKUy0Xpzz9TsGABZevWo5nN+F0yjcCbbsKzf/+WHp4QQgghhDgJCXDi7Kx6AoxmGPdwncOeFiPl9rozcJqpkICO3zHrx13E+sfy0ZSPGBw+uDlHK6o4CwooXLKEwv9+hiMjA1N4OCFz5hAwYzqmoKCWHp4QQgghhPgNpxXgNE2bCrwOGIH3lVLPnfD43cAtgBPIAf6slDpS9ZgL2FV16lGl1BWNNHbRUlI3wZ4vYcwD4Bteffj7XZmU211Y/RMwBS9HMxei3B5omgPNaGLOgDn8ofcfMBvNLTj49qli9x69d9t336HsdrwuuojQB+7Hd/x4NJO8jyOEEEII0Vb85is3TdOMwJvAJCAN2Kxp2tdKqb21TksABiulyjVN+zvwAnB91WMVSqkLG3ncoqUoBSseAZ8wGHFH9eHMogoeXLqLmK6JFHp+hUPZANCMlWgYuGvwbH7f+/ctNep2yW23U7J8OQWfLqBixw40Ly/8r72GDjNnYu3evaWHJ4QQQgghzsLpvPU+FDiklEoG0DTtM+BKoDrAKaV+qnX+BuB3jTlI0Yrs+wZSN8Ll88Gq9wFzuxX3Lt6B3emGwGU4Kmx1LlG4+c/e/0iAayaOrCwKFi2i8PPFuPLysHTuTNhDD+F/9VUYfX1benhCCCGEEOIcnE6AiwJSa32dBlx0ivP/Aiyr9bWHpmlb0JdXPqeU+urECzRNuw24DaBTp06nMSTRIpx2+OFxCOmlFy+p8uHaw6w9lMez1/Tj2X3HGrw0qyyruUbZLimlKN+8mYIFCyn54Qdwu/EZO5bAm27Ce8RwNINU+RRCCCGEOB806uYXTdN+BwwGxtQ63Fkpla5pWgzwo6Zpu5RSSbWvU0q9B7wHMHjwYIVonbZ8CPnJcNOS6qbdezOKeSE+kUm9w7jkAn+e32/ApVz1Lg33Dq93TJw7d1kZRd98S8GCBdgOHsTo70/Qn24m4IYbsERHt/TwhBBCCCFEIzudAJcOdKz1dXTVsTo0TZsIPAyMUUpVr6FTSqVXfUzWNO1nYACQdOL1opWrKIRfnoOYsXrjbqDS4WLOogT8vcw8c00f7v/1LtzKjcVgwe62V1/qYfRg9sDZLTPu85Q9JYWC//6XwqVf4i4pwdq7FxFPP43fpZdg8PBo6eEJIYQQQogmcjoBbjPQXdO0rujB7QZgZu0TNE0bALwLTFVKZdc6HgiUK6VsmqYFAyPRC5yItmbNK3qImzQPNA2A5+P3c+BYKf/+81D+k/g2a9PX8uiwR/E2e/P6ttfJKssi3Duc2QNnc2nMpS38BNo+5XJRuno1BQsWUrZmDZjN+E2ZQuBNM/G88EK0qp+LEEIIIYQ4f/1mgFNKOTVNmwUsR28j8KFSao+maU8CW5RSXwMvAj7A4qoXkcfbBfQC3tU0zQ0Y0PfA7W3wG4nWq+AIbHgHLrgRIvQmz6sP5PDR2hRuHtGFUtMmPtr9Edf1uI7r4q4DkMDWiFyFhRR+sZSC//4XR1oaptBQgu+8g8AZMzCFhLT08IQQQgghRDM6rT1wSqnvge9POPZYrc8nnuS6dUC/cxmgaAV+rJp1G/8IAPlldu5ZvIMeYT5ceZGbW1c+zsDQgTww9IEWHuj5pXLfPvIXLKD4m29RNhtegwcTeu89+E6YgGaWXnpCCCGEEO2RdPAVp5a+DXYthlH3gn8USike+GInReUOXp/ZjftW30KgRyCvjH1FGnSfA0d2Nul330PUC89TnpBAwYKFVGzbhubpif+VVxJ400w84uJaephCCCGEEKKFSYATJ6cUrHgUvEPg4jkAfL4llRV7j3H/tFje3f84hbZCPpn2CUGeQS082LYt++WXqdiyhUOXXAKVNsydOhH6wP0EXH01Rn//lh6eEEIIIYRoJSTAiZNLXAZH1sClL4PVl8O5ZTzxzV5GxAaRY/mcbSnbeH7U8/QO6t3SI20zlMuF/ehRbPv3U7k/Edv+/VTs3YsrJ0c/we4g8sUX8Lv0UundJoQQQggh6pEAJxrmcsDKxyC4Bwz8Iw6XmzmLtmM2Ghg35BDzdyzmT33/xCUxl7T0SFstV2kZtgOJVO7fj21/IpWJ+7EdOIiqqNBPMBqxxnTF4OWFy2gElwuMRsq3JeB/+eUtO3ghhBBCCNEqSYATDdv6MeQdhBs/A6OZ/1t5gB2phdx7hZm3dr7MyKiRzB4gvd0AlFI40tNrZtUS9Y+O1NTqcwz+/njExREwYzoecT2x9ozD2q0brqIikiZN1sMbgMNB0dKlhPzj71JhUgghhBBC1CMBTtRXWQw/PwddRkGPqWw9ks8bPx5k2oVWFqfOI8o3ihdGv4DRYGzpkTY7d2UltoMH9Vm1ffupTEzElpiIu7RUP0HTsHTujEefPgRcew3WuDg8evbEFB7eYJ+2Y2+9jXK76xxTbjc5b71NxOOP1TtfCCGEEEK0bxLgRH1rX4PyXJg8jxKbkzmLthMZaCTH611spTY+GvcRfha/lh5lk1JK4czOrjerZk9JgarAZfDywhoXh9/ll+ER1xOPXj2xdu+OwcvrtL9Pxfbt4HDUPehwUJGQ0IjPRgghhBBCnC8kwIm6itJg/ZvQ7zqIHMATi3eQXlDOhNGr2JidyPzx84kJiGnpUTYqZbdjS0qqLipSmah/dBUWVp9jjorC2rMnflOnYu2pz6qZo6PPudBIzFdfnuvwhRBCCCFEOyIBTtT141N6+4AJj/L9rkyWbE1j3NC9bMj+gTsH3MnYjmNbeoTnxJmfX3dWbd9+bMnJ4HQCoFmtWHv0wHfSRKxxPfHoGYc1Lg6jr28Lj1wIIYQQQggJcKK2zB2w4zMYOZtMLYQHl/5K9y7pbC35lMmdJ3NLv1taeoSnTTmd2FNS6ix/tO3fj/N4uX7AFBqKtWccPmPG6LNqvXph6dwZzdj+9vYJIYQQQoi2QQKc0CkFKx4Bz0DcI+/i3oU7cBiOUeL7Ed19uzNv5LwGi3C0Bq7iYmyJiVTuT6Ry/z5s+xOxHTqEstn0E8xmrLGxeI8YgbVn1axaz56YAgNbduBCCCGEEEKcIQlwQndwJRxeDdNe4MOt+axNTqdLv4W4NBPzx8/Hy3z6hTmainK7caSm1ptVc2RkVJ9j7NABj55xBM6cWR3UrF27olksLThyIYQQQgghGocEOAEuJ6x8FDrEsjfyWl54ZyOd4r6i0JHJvyb/iyifqGYfkrusjMoDB6pm1vRG2LYDB3CXl+snGAxYunbF88ILCbjhhqq9aj0xhYa02plCIYQQQgghzpUEOAEJ/4Gc/div/YQ5S/bgFb6KArbz4JAHGRI+pEm/tVIKZ2ZmnVm1yv37cBxN1Zd1AgZfXzzi4vC/5prqoGbt3g2Dh0eTjk0IIYQQQojWRgJce2crhZ+egY7DePZwLMnl3+AZvZJrul/DjT1vPOvbOrKzSb/7HqJffQVTSAgAbpsN28FDdZY/Vh44gLuoqPo6c+dOeMT1xP/KK/Ho2ROPuDhMkZEyqyaEEEIIIQQS4MS6+VCWTcLIt/j38vX4xXxB35ALePiih88pNGW/+hoVW7eSOusOLNHRVCbux344BVwuADQvLzy6d8dv6tTqvWoePXpg8PZupCcmhBBCCCHE+UcCXHtWnAFr52PreRW3/lKGX+cFdPD049Wxr2Ixnn3Rj6Jvv6P4S71BdeWOHTiyMvHs3QffSZPwqOqtZu7U6ZybYAshhBBCCNHeSIBrz356GqVcPFZ2NRUBH2M1l/D6uI8J8Qo561vajxwh48EHaw6YzfiOn0DE4481woCFEEIIIYRo32QKpL3K2g0JCzjQ6Qa+Kl6GwSuJuSMep19Iv7O+pau0jKN//Ss4HDUHHQ6Kli6t00BbCCGEEEIIcXYkwLVXKx/DZfXnpmw/LB3W8btev+eK2CvO+nbK7Sbj/vtxpBwBo7HeYzlvvX2uIxZCCCGEEKLdkwDXHh36AZJW8YzHJGwh3zEgZCj3DL77nG6Z+8ablK5ahSksrLpQSTWHg4qEhHO6vxBCCCGEEEL2wLU/bheseIyDXlEs8ttDkDWU/5vwCibD2f9TKF6xgty33sL/6quJeOZpKfkvhBBCCCFEE5EZuPZmx3+x5ezhj37BmEwO3p/6Jv5W/7O+XWXiATIeeBCPC/oTPvdxCW9CCCGEEEI0IZmBa0/sZbhWzePu4C6UeBbw7MiX6B7Y/axv5ywoIO322zF6exM9//8wWK2NOFghhBBCCCHEiSTAtSfr32ShoYzVPhau7PwnLus25axvpZxO0u++G+exY3T+9D+Yw0IbcaBCCCGEEEKIhkiAay9KjrFm4xu8FBpIJ+tQnhwz55xul/3ii5Sv30DE00/jecEFjTRIIYQQQgghxKnIHrh2IjH+Ye4P9sXDHcKCq17DoJ39j77wy6/I//cnBP7h9wRce00jjlIIIYQQQghxKhLg2oHS9AQeKFyDHTOvTnibAA/fs75XxY4dZD3+OF7DhhH2z3824iiFEEIIIYQQv0UC3HnOrdzcveyvJJtNXBd9HyM6x531vRzHskmbdQem0FCiXn0FzSQrcIUQQgghhGhO8gr8PPf8sntZb65gckUv7p0086zv47bbSb/zTlxlZXR5/31MgYGNOEohhBBCCCHE6ZAAdx6LT17BwpyVTCxx8tDv3j/rHm1KKbLmPkHFjh1Evf46HnE9GnmkQgghhBBCiNMhSyjPU4cKDvHw6vvpa7Nxc/c5BAWcfbPugk8XULR0KcH/+Dt+UyY34iiFEEIIIYQQZ+K0ApymaVM1TUvUNO2QpmkPNPD43Zqm7dU0baemaas0Tetc67E/app2sOrPHxtz8KJhRbYiblv+D3xcdh4o9ueCSbee9b3KNmzg2HPP4TN+PMGzZjXiKIUQQgghhBBn6jcDnKZpRuBNYBrQG7hR07TeJ5yWAAxWSvUHlgAvVF3bAXgcuAgYCjyuaZpsnmpCTreTOT/eQ15lFq9nZ9PzqpfBcHYTrfa0NNLn3IWlSxciX3ge7SzvI4QQQgghhGgcp/OKfChwSCmVrJSyA58BV9Y+QSn1k1KqvOrLDUB01edTgJVKqXylVAGwEpjaOEMXDXl166tsyd7I/bnFxISPxdpt9Fndx11WRtrts1BuNx3ffAOjj08jj1QIIYQQQghxpk4nwEUBqbW+Tqs6djJ/AZadybWapt2madoWTdO25OTknMaQREO+SfqGT/Z+woDCAG4oK8bv8mfO6j5KKTIeehjbwYNEvfwyli5dGnegQgghhBBCiLPSqGviNE37HTAYePFMrlNKvaeUGqyUGhwSEtKYQ2o3dufu5vF1c7FWRPNewW60QX+C4O5nda+8d9+lZPlyQu+5B59RFzfySIUQQgghhBBn63QCXDrQsdbX0VXH6tA0bSLwMHCFUsp2JteKc5Nbkcvsn2ajnD48lm3DYvFCG1uv1sxpKfnxR3Jeex2/yy+nw5//1MgjFUIIIYQQQpyL0wlwm4HumqZ11TTNAtwAfF37BE3TBgDvooe37FoPLQcma5oWWFW8ZHLVMdFI7C47d/10F/kVRXQ8MpIr3FsxjLobfM58JtOWlETGff/Eo08fIuY9edZ944QQQgghhBBN4zcbeSulnJqmzUIPXkbgQ6XUHk3TngS2KKW+Rl8y6QMsrnrRf1QpdYVSKl/TtHnoIRDgSaVUfpM8k3ZIKcUzG59he852bOk38obH/8ASBcP+ccb3chUVkfaP29E8PIh+4/8weHg0wYiFEEIIIYQQ5+I3AxyAUup74PsTjj1W6/OJp7j2Q+DDsx2gOLlFiYv44uAXWEsncS0OOlXuh6nvgNnzjO6jXC7S77kXe0YGnT/+CHNERBONWAghhBBCCHEupLFXG7U5azPPb3qeYMMAStNG8qB5EYT3g/7Xn/G9sl95hbI1awh/9BG8Bg1qgtEKIYQQQgghGoMEuDYoozSDe36+h0BLBIf3Xcl7PRKwlqXD5KfPuGl30Tffkv/BhwTceAOB113XRCMWQgghhBBCNIbTWkIpWo9yRzmzf5qN3eWgMOUmLo7wYlTWv6H7ZIgZc0b3qti9h8xHHsFr8GDCH3ywiUYshBBCCCGEaCwyA9eGKKV4fN3jJOYnElL5Z5y2IN6M/gHNXgqTnjyjezlzc0mbNQtjUAeiXn8NzWJpolELIYQQQgghGosEuDbkg90fEJ8Sz4jAP7DrYCQvjPPGf9fHMPAPENrrtO+j7HbSZs/BVVhIxzfewBQU1HSDFkIIIYQQQjQaCXBtxOq01czfNp+R4ZP4cWNvJvcO49Ls98BohbEPndG9sp5+hoqtW4l4+ik8evduohELIYQQQgghGpsEuDYguSiZ+1ffT4/AOJL2TsPfy8JLwyrR9n0NI2eDb9hp36vgs88oXLSIoFtvxf/SS5tw1EIIIYQQQojGJgGulSu2FzP7x9lYjBZi3LM4eMzOS9P747d6LviEw4hZp32v8i1byHrqabxHjyJkzuymG7QQQgghhBCiSUiAa8VcbhcPrH6AtJI0/hD7GJ9vKOXmEV0Y41wLaZth/CNg8T6tezkyMki7czaW6GiiXnoJzWhs4tELIYQQQgghGpsEuFbs/xL+j1/Tf+XOC+/jneWKHmE+PDCpK/wwF0L7wIUzT+s+7ooKUmfNQtntRL/1JkY/v6YduBBCCCGEEKJJSB+4Vir+cDwf7P6AGT1msH57D4rKc/j3n4bisf1jKEiB330Bht+eRVNKkfnIo9j27Sf67bewxsQ0+diFEEIIIYQQTUNm4FqhfXn7eHTtowwMHUis4SZW7D3GfVPi6B3ogl+eh9jx0G3iad0r6e1YQAAAIABJREFU/4MPKP7uO0LmzMF37NimHbgQQgghhBCiSUmAa2XyKvKY/dNs/K3+zO4/j6e+PciI2CD+cnFXWP0SVBbBpHmnda/SX38l++VX8LtkGkG33drEIxdCCCGEEEI0NVlC2Yo43A7u+eUe8ivz+WDyxzy2OBWLycDL112AoegIbHoPBtwE4X1/8162w4dJv/serHFxRDz1FJqmNcMzEEIIIYQQQjQlmYFrRZ7f9Dxbj23liRFPsGq7mR2phTxzdT8i/D1h1ZOgGWHcw795H1dpKWm3z0IzmYh+4w0MXl7NMHohhBBCCCFEU5MA10osObCERYmL+FOfPxFuHM4bPx3i2oHRXNo/AtK2wO4vYMQd4Bd5yvsot5uMe+/DfvQoUa+/hiU6qpmegRBCCCGEEKKpSYBrBRKyE3h649OMjBrJn3v/gzmLthMV6MncK3qDUrDiEfAOhZF3/ua9cubPp/Tnnwl78AG8hw5thtELIYQQQgghmosEuBaWVZbFXT/dRZRPFM+Pep553yaSXlDBq9ddiK+HGfZ/C0fXw7iHwOp7ynsVx8eT9867BMyYTuDM0+sRJ4QQQgghhGg7pIhJC6p0VjL7p9lUuir5cNyHrEks54ttadw5vhuDu3QAlwNWPg7BcTDg96e+1/79ZDz4EJ4DBhD26KNStEQIIYQQQojzkAS4FqKU4on1T7Avbx/zx8/HU4vgoS9/5YKOAdwxobt+0paPID8JZn4OxpP/qJwFBaT943aMfn5Ez38dg8XSTM9CCCGEEEII0ZwkwLWQT/Z+wrfJ33LHgDsYHTWG33+4EYfLzWvXX4jZaND7vf38LHQdDd0nn/Q+yuEgffYcnLm5dF7wKaaQkGZ8FkIIIYQQQojmJAGuBaxNX8srW19hUudJ3NrvVt7/9TBrD+Xx3DX96BrsrZ/06ytQUQCTn4JTLIc89vwLlG/aROTzz+HZr18zPQMhhBBCCCFES5AiJs3saPFR7lt9H90CuvHUyKfYl1nCi8sTmdw7jOuHdNRPKkyFDW9D/+sh4oKT3qtwyRIKPv2UDjffjP+VVzbTMxBCCCGEEEK0FAlwzajUXsodP96BUTPy+rjXMWBlzqIE/L3MPHdt/5rCIz/O02fdxj9y0nuVJySQ+cSTeI8YQei99zTTMxBCCCGEEEK0JAlwzcSt3Dy45kGOFB/h5TEvE+0bzXPL9nPgWCkvzbiADt5VhUcyEmDnIhj2Dwjo2OC9HMeOkXbnnZgjIoh65WU0k6yEFUIIIYQQoj2QV/7N5O0db/Nz6s88MPQBhkYM5ZcDOXy8LoWbR3RhTI+qwiNKwYpHwSsYLr6rwfu4bTbSZt2BKiun44cfYgwIaMZnIYQQQgghhGhJEuCawcojK3lnxztc3e1qZvacyf+3d+/hUVVn38e/d0IgATkJKCGhJIoFgUROohDhUayAgAjWViqgqO/TFkXwhOKRaLEcrYKASn0UvNSqBUUUrVgJraAFAsRwUkCkkBAgHBJICJDDev+YEHNOCEkmGX6f6+KamTV7r33vWXvIvmetvfaR9NM88vfv+OXFFzDxxvY/L7j9H7D7axg4EwIbFanHOcf+ZyZxctMmQue8TL3LLqvGvRAREREREW9TAlfFth/dzpOrniSyRSRPXe25pm3i4nhST2Sy8K4eBAb4exbMzoIvn4FmbaHb6GLrOvrWW6R+/DHN7x9Lw1/9qpr2QEREREREagolcFUo5WQK41aMo2FAQ1669iXq+tflvbV7WL71AE8OvJwOrfL1sm1YCIe2w/B3wT+gSF3p33zDgWnTaXjDDTQfM6Ya90JERERERGoKJXBVJCsni0f+9QjJJ5JZMGABLeq34KdD6Tz7yVZ6XdqMe64J/3nhk8c8N+1uEwXtBhap6/SePSQ8+BD1Lr2UVlOnYH6ae0ZERERE5HykBK6KvBD7Amv2r2Fy1GQiWkSQmZ3DA+/HUbeOHy/89gr8/PLdnHv1LEhPhtvfL3LT7uy0dBLuuw8DQufNxa9Bg+rdERERERERqTGUwFWBJTuX8Pa2txl5+Uhubuu5wfbLX+3gu70pzL29K8GNg35eODURvp0LnW6FkG4F6nE5OSQ9PpFTu37iF6//lbqti7+tgIiIiIiInB/KNRbPzAaY2Q9mttPMJhbzfh8z22BmWWZ2a6H3ss0sLvff0soKvKaKT47nuW+f46rgq3i4u+cG27G7jzAnZie/7hrKoMjggivEPA8uG65/pkhdh+a9wvEv/8nFj06gQc+e1RG+iIiIiIjUYGX2wJmZPzAXuAFIANaZ2VLn3NZ8i+0BRgOPFFNFhnOucyXEWuMdPHGQB2Ie4KL6FzGzz0zq+NXh+MlMHvwgjpCmQUQP6VBwhaR4iHsXet0PTdsUeOv4P//JoTlzaDx0KE3vuKMa90JERERERGqq8gyh7AHsdM7tAjCz94CbgbwEzjm3O/e9nCqIsVY4lX2KB2MeJC0zjbdveJsmgZ4bbEcv3Uri0Qw++ENPGgbmm13SOfjyaQhqAr0fLlDXye3b2ffoYwRGRtLy2Wis0HVxIiIiIiJyfirPEMoQYG++1wm5ZeUVaGaxZvYfMxta3AJm9vvcZWKTk5PPouqawTnH5P9MJv5QPH++5s/8sukvAVgWn8TiDQmMva4t3cMuLLjSzq9g10r4n8c8SVyu7JQUEu4bizWoT+jLL+NXr1417omIiIiIiNRk1TGJSRvnXKKZXQKsMLNNzrkf8y/gnJsPzAfo3r27q4aYKtW737/Lkp1L+OMVf+RXbTw32E5KzeCJjzZxResm3H/9ZQVXyM6C5U9B03Dofk9escvKIvGhh8nav59fvLWQgIsvqs7dEBERERGRGq48PXCJQP7pD0Nzy8rFOZeY+7gLWAl0OYv4arw1SWuYsW4G17W+jjFXeG6wnZPjeOTv35GZncNLt3UmwL/Qxxz3DiRvgxuehTp184oPznyB9G++oWV0NPW7+NTHJCIiIiIilaA8Cdw64DIzCzezusBwoFyzSZpZUzOrl/u8ORBFvmvnaruE4wk8/K+HCW8czpTeU/Azz8f5f6t+YvXOwzwzuAPhzQvdt+1UmmfmydZXweVD8opTlizhyIIFNB05kia/vqU6d0NERERERGqJMhM451wWMBb4AtgGfOCc22Jmz5nZEAAzu9LMEoDfAK+Z2Zbc1S8HYs3sOyAGmFpo9spa60TmCcbHjCfH5TDrulk0CPAkalv3HWPGFz/Qr8PF3HZlMfdt+3YOpB2AfpPzbtqdsWkT+5+ZRP2rruLixx6tzt0QEREREZFapFzXwDnnPgM+K1T2TL7n6/AMrSy83jdAxDnGWOM453hq9VPsTNnJK9e/wi8a/QKAk5nZPPD+RhrXD2DqryOLzh55fD+sngUdhkLrHgBkHjxIwn1jqdOiBSEvvYgFBBTenIiIiIiICFA9k5j4nPnx8/nyv1/ySPdH6BXSK6986uffs/1AGgvv7sGFDeoWXTHmecjOhF9NAiDn9GkSx40n+/hxwt77G3WaNq2uXRARERERkVpICdxZitkTw5y4OQy+ZDB3dPj5Btv/2p7Mgm92M7pXGP/zyxZFVzywFTa+DVf9ES68BOcc+597joy4OEJeepHAdu2qcS9ERERERKQ2UgJXhmW7ljFrwyz2p++neVBzUk+l0rFZRyb1nJQ3RPJI+mke+ft3/PLiC5h4Y/viK/ryGajXEPpMAODou++Sumgxzcb8kUYDBlTX7oiIiIiISC2mBK4Uy3YtI/qbaE5mnwQgOcNzk/GbLr2JwDqBgOd6uImL40k9kcnCu3oQGOBftKIfV8DOLz0Tl9S/kPQ1azkwZSoXXHcdLe6/v9r2R0REREREarfy3EbgvDVrw6y85C2/hVsW5j1/f91elm89wIT+7ejQqlHRSnKyYfnT0OQX0OP3nE5IJPGBB6jbpg2tZkzH/NQEIiIiIiJSPuqBK8X+9P2llv90KJ1nP9lKVNtm3HNNePGVfPceHNgMt75BzulsEsaOxWVn03ruHPwvuKCqQhcRERERER+k7p9StGzQssTyzOwcHng/jrp1/Jj5myvw87OiC54+ASsmQ0g3XIdh7HvySU5t307ICzOpGxZWtcGLiIiIiIjPUQJXivFdxxPoH1igLNA/kPFdx/PyVzv4bm8Kfx4WQXDjoOIr+M9cOL4P+k3m8F9f5/jn/+Cihx/igt69qyF6ERERERHxNRpCWYpBlwwCyJuFsmWDlozvOp6L/Xpyf8y3/LprKIMig4tfOe0grHoJ2g/m+E+nSX7pJRoNHsyFd99djXsgIiIiIiK+RAlcGQZdMigvkQM4fjKTgbO/JqRpENFDOpS84sopkHWSU5f9P/b9YQKBl19O8OQ/5d16QERERERE5GxpCOVZil66lcSjGbz42840DAwofqHkH2D9QrI7jiLhyelYvXqEznkZv8DA4pcXEREREREpB/XAlWHJxkRmfPED+1IyaFI/gKMnMhnXty3dwy4seaUvJ+HqNCDxs1ROJybSZsGbBLRqVX1Bi4iIiIiIT1ICV4olGxN5/MNNZGRmA3D0RCZm0KZZ/ZJX+unfsP1zklMHkr76P7SMjqZ+t27VFLGIiIiIiPgyDaEsxYwvfshL3s5wDv7y5Y7iV8jJgeVPkZocwuHP42gy/DaaDr+tGiIVEREREZHzgRK4UuxLyTircjYv4uTWLSR97U9Q9260fOKJKoxORERERETON0rgStGqSfH3dyu2PDODrE+fZe83LfG/sDmhs2ZhdetWcYQiIiIiInI+UQJXign92xEU4F+gLCjAnwn92xVZ1q2eS8LnGWSf8id07hzqNGtWXWGKiIiIiMh5QpOYlGJolxCAvFkoWzUJYkL/dnnledIPsf/FV8lIrkermVMI6tjRC9GKiIiIiIivUwJXhqFdQoombIUcnXovKT8E0Oz2YTQePKjUZUVERERERCpKQyjP0YmvPmb/4ngatGtOiyf/5O1wRERERETEh6kH7hxkJiWR8OhT1G2QQ8irCzB//7JXEqkmmZmZJCQkcPLkSW+HIiIi54nAwEBCQ0MJCAjwdigiPksJXAXlnDxJwu/vwp06TejTv8E/+FJvhyRSQEJCAg0bNiQsLAwz83Y4IiLi45xzHD58mISEBMLDw70djojP0hDKCnDOkfT005zc8V9a9fWj3rAnvR2SSBEnT56kWbNmSt5ERKRamBnNmjXTyA+RKqYeuAo48sabHPvkU1pEHKPhXdOhbn1vhyRSLCVvIiJSnfR3R6TqqQfuLKV9vYqDL7xAw0uMZteFwRXDvR2SiIiIiIicJ5TAlUPmwYPsHjmKExs2kPjww9Rr1ZRWXfdh/SeDnyYuEd+wZGMiUVNXED5xGVFTV7BkY6K3Q5LixH8AL3aC6Caex/gPvB2RnKVlu5bRb1E/IhdG0m9RP5btWubtkKQCzpwbZCUnV0p9u3fvplOnTpVSV2ErV65k8ODBACxdupSpU6dWuK6wsDAiIiLo3Lkz3bt3r6wQReQsKIErh0PzXiFj/Xr2/nEM5meEXrUXv/bXw6XXeTs0kUqxZGMij3+4icSUDByQmJLB4x9uqtIkbuDAgaSkpJCSksK8efPyyvOfaNQko0ePJjw8nM6dO9O5c2fi4uKqP4j4D+CTcZC6F3Cex0/GVXkSV9vaas6cObRt2xYz49ChQ3nlzjnGjRtH27ZtiYyMZMOGDdUe27Jdy4j+Jpqk9CQcjqT0JKK/ia7SJK62tV9J37Wa0H75nTk3SJ73ilfjOFtDhgxh4sSJ51RHTEwMcXFxxMbGVlJUInI2dA1cGTIPHiT1o4/AOXKOHSNkdA/qnvoBbtA936T2ePaTLWzdd6zE9zfuSeF0dk6BsozMbB5dFM/f1u4pdp0OrRox6aaOFY7ps88+Azy/Os+bN4977723wnVVVFZWFnXqlP+/wRkzZnDrrbdWXUCfT4T9m0p+P2EdZJ8qWJaZAR+PhfULi1+nZQTcWPFf26H2tVVUVBSDBw/m2muvLVD++eefs2PHDnbs2MGaNWsYM2YMa9asqdQ4p62dxvdHvi/x/fjkeE7nnC5QdjL7JM+sfoZF2xcVu077C9vzWI/HKhxTbWs/KP67Vh3tB7D/z3/m1LaS2xDAnT5NRnw8OEfKe+9xats2rJRp8+td3p6WTzxR5razsrIYMWIEGzZsoGPHjrz11lvMnDmTTz75hIyMDHr16sVrr72GmTF79mxeffVV6tSpQ4cOHXjvvfdIT0/n/vvvZ/PmzWRmZhIdHc3NN99cYBsLFiwgNjaWOXPmMHr0aBo1akRsbCz79+9n+vTpeZ/7jBkz+OCDDzh16hTDhg3j2WefLcenJyLVQT1wZTg07xVcVpbnhb8f6av/BV1GwsUdvBuYSCUqnLyVVV4eM2bMYPbs2QA8+OCD9O3bF4AVK1YwYsQIwsLCOHToEBMnTuTHH3+kc+fOTJgwAYC0tDRuvfVW2rdvz4gRI3DOlbidsLAwJk2aRNeuXYmIiOD77z0nXkeOHGHo0KFERkZy9dVXEx8fD0B0dDSjRo0iKiqKUaNGER0dzZ133knv3r1p06YNH374IY8++igREREMGDCAzMzMCn8Gla5w8lZWeTn5Wlt16dKFsLCwItv/+OOPueOOOzAzrr76alJSUkhKSjqnz+5sFU7eyiovD19rv5LUhPY74/S+fQVfJ1bOaIUffviBe++9l23bttGoUSPmzZvH2LFjWbduHZs3byYjI4NPP/0UgKlTp7Jx40bi4+N59dVXAXj++efp27cva9euJSYmhgkTJpCenl7qNpOSkli1ahWffvppXs/c8uXL2bFjB2vXriUuLo7169fz73//G/BMUtKvXz+6devG/PnzK2W/ReTsqAeuFHm9b9nZnoLsHFJ3BdIiYow+OKlVyuopi5q6gsSUjCLlIU2CeP8PPSu0zd69e/PCCy8wbtw4YmNjOXXqFJmZmXz99df06dOH1atXA56TkM2bN+cNk1q5ciUbN25ky5YttGrViqioKFavXs0111xT4raaN2/Ohg0bmDdvHjNnzuT1119n0qRJdOnShSVLlrBixQruuOOOvG1s3bqVVatWERQURHR0ND/++CMxMTFs3bqVnj17snjxYqZPn86wYcNYtmwZQ4cOBeDJJ5/kueee4/rrr2fq1KnUq1evQp9NicrqKXuxU+7wyUIat4a7Kj4EzxfbqjiJiYm0bt0673VoaCiJiYkEBwdX+LMrrKyesn6L+pGUXjTpCG4QzJsD3qzQNn2x/Yr7rlVH+wFl9pRlHjzIjzf0gzPJ7pkROn95gTotWpzTtlu3bk1UVBQAI0eOZPbs2YSHhzN9+nROnDjBkSNH6NixIzfddBORkZGMGDGCoUOH5n1uy5cvZ+nSpcycORPw3E5mz57iR1GcMXToUPz8/OjQoQMHDhzIq2f58uV06dIF8CT6O3bsoE+fPqxatYqQkBAOHjzIDTfcQPv27enTp8857beInB31wJXi0LxXcNlZBcocfiS/VfwwF5HaakL/dgQFFJyQJyjAnwn921W4zm7durF+/XqOHTtGvXr16NmzJ7GxsXz99df07t271HV79OhBaGgofn5+dO7cmd27d5e6/C233JK3zTPLrlq1ilGjRgHQt29fDh8+zLFjnmGkQ4YMISgoKG/9G2+8kYCAACIiIsjOzmbAgAEARERE5NU3ZcoUvv/+e9atW8eRI0eYNm3a2X4k5+76ZyAgqGBZQJCn/Bz4WlvVZOO7jifQP7BAWaB/IOO7jq9wnb7WfjXiu1aKQ/NeweUUHJ3gcnIq5Vq4wlPwmxn33nsvixYtYtOmTfzv//5v3j3Wli1bxn333ceGDRu48sorycrKwjnH4sWLiYuLIy4ujj179nD55ZeXus38P0Sd6YF1zvH444/n1bNz507uueceAEJCQgC46KKLGDZsGGvXrj3n/RaRs6MErhQZ/1kJWdkFC7MdGd/GeCUekaoytEsIU26JIKRJEIan523KLREM7RJS4ToDAgIIDw9nwYIF9OrVi969exMTE8POnTvP6oTC39+frKysUpb+efnyLAvQoEGDYtf38/MjICAg7yTKz88vr77g4GDMjHr16nHXXXd556Ql8rdw02xPjxvmebxptqf8HPhaW5UkJCSEvXt/7sFMSEjIOxmtLoMuGUR0r2iCGwRjGMENgonuFc2gSwZVuE5fa7+Svms1of0AMuLioPBwz8xMMjZuPOe69+zZw7fffgvAu+++m9cb2rx5c9LS0li0yPMDck5ODnv37uW6665j2rRppKamkpaWRv/+/Xn55ZfzErGNFYypf//+vPHGG6SlpQGe3uuDBw+Snp7O8ePHAUhPT2f58uVVNnOmiJSsXCMBzWwAMAvwB153zk0t9H4f4CUgEhjunFuU7707gadyX052zpVwpX3Nc0n/ZEjdV/SNxrp1gPieoV1CzilhK07v3r2ZOXMmb7zxBhERETz00EN069atwK/MDRs2zDshqOxtv/POOzz99NOsXLmS5s2b06hRowrXl5SURHBwMM45lixZ4r2TlsjfnnPCVhxfaquSDBkyhDlz5jB8+HDWrFlD48aNK334XXkMumTQOSVsxfGl9ivpu1ZT2u+SJR9VWd3t2rVj7ty53H333XTo0IExY8Zw9OhROnXqRMuWLbnyyisByM7OZuTIkaSmpubNztmkSROefvppHnjgASIjI8nJySE8PDzvmrmz0a9fP7Zt20bPnp4h9BdccAFvv/02aWlpDBs2DPBMuHL77bfn9aKKSPUpM4EzM39gLnADkACsM7Olzrmt+RbbA4wGHim07oXAJKA74ID1ueserZzwq1hqwtmVi0gBvXv35vnnn6dnz540aNCAwMDAIkO6mjVrRlRUFJ06deLGG29k0KDKObGNjo7m7rvvJjIykvr167Nw4bn9djRixAiSk5NxztG5c+e8SQN8hS+11ezZs5k+fTr79+8nMjKSgQMH8vrrrzNw4EA+++wz2rZtS/369XnzzYpdc1YT+VL7lfRd8+X2A88kMWcmhslv8uTJTJ48uUj5qlWripQFBQXx2muvFSm/9tpr82ZlHT16NKNHjwY8M1Lmd6bHDWD8+PGMH190aO93331X2m6ISDWw0macAjCznkC0c65/7uvHAZxzU4pZdgHw6ZkeODP7HXCtc+4Pua9fA1Y65/5W0va6d+/uasx9RUqbMODBzdUfj8hZ2LZtW5nDp0RERCqb/v6InDszW++c617ce+W5Bi4EyJ/FJOSWlUe51jWz35tZrJnFJicnl7PqalBFEwaIiIiIiIhURI2YDd85Nx+YD54eOC+H87Mz15l89Zxn2GTjUE/yVgXXn4hI6YYNG8ZPP/1UoGzatGn079/fSxFJSdRWtZvaT0SkZitPApcItM73OjS3rDwSgWsLrbuynOvWDFU0YYBIdXDOFZmWurb66KOqmzhAKpfaqnZT+8m5KOvSHBE5d+UZQrkOuMzMws2sLjAcWFrO+r8A+plZUzNrCvTLLRORKhYYGMjhw4f1x1RERKqFc47Dhw8TGBhY9sIiUmFl9sA557LMbCyexMsfeMM5t8XMngNinXNLzexK4COgKXCTmT3rnOvonDtiZn/CkwQCPOecO1JF+yIi+YSGhpKQkECNuq5URER8WmBgIKGhod4OQ8SnlTkLZXWrUbNQioiIiIiIVLNznYVSREREREREagAlcCIiIiIiIrWEEjgREREREZFaosZdA2dmycB/vR1HMZoDh7wdhPg0HWNSlXR8SVXS8SVVSceXVKWaeny1cc61KO6NGpfA1VRmFlvShYQilUHHmFQlHV9SlXR8SVXS8SVVqTYeXxpCKSIiIiIiUksogRMREREREakllMCV33xvByA+T8eYVCUdX1KVdHxJVdLxJVWp1h1fugZORERERESkllAPnIiIiIiISC2hBE5ERERERKSWUAJXDmY2wMx+MLOdZjbR2/GI7zCz1mYWY2ZbzWyLmY33dkzie8zM38w2mtmn3o5FfI+ZNTGzRWb2vZltM7Oe3o5JfIeZPZj793Gzmf3NzAK9HZPUXmb2hpkdNLPN+couNLMvzWxH7mNTb8ZYHkrgymBm/sBc4EagA/A7M+vg3ajEh2QBDzvnOgBXA/fp+JIqMB7Y5u0gxGfNAv7hnGsPXIGONakkZhYCjAO6O+c6Af7AcO9GJbXcAmBAobKJwFfOucuAr3Jf12hK4MrWA9jpnNvlnDsNvAfc7OWYxEc455Kccxtynx/Hc+IT4t2oxJeYWSgwCHjd27GI7zGzxkAf4P8AnHOnnXMp3o1KfEwdIMjM6gD1gX1ejkdqMefcv4EjhYpvBhbmPl8IDK3WoCpACVzZQoC9+V4noBNsqQJmFgZ0AdZ4NxLxMS8BjwI53g5EfFI4kAy8mTtM93Uza+DtoMQ3OOcSgZnAHiAJSHXOLfduVOKDLnbOJeU+3w9c7M1gykMJnEgNYGYXAIuBB5xzx7wdj/gGMxsMHHTOrfd2LOKz6gBdgVecc12AdGrB8COpHXKvRboZzw8FrYAGZjbSu1GJL3Oe+6vV+HusKYErWyLQOt/r0NwykUphZgF4krd3nHMfejse8SlRwBAz241n+HdfM3vbuyGJj0kAEpxzZ0YOLMKT0IlUhl8BPznnkp1zmcCHQC8vxyS+54CZBQPkPh70cjxlUgJXtnXAZWYWbmZ18Vw8u9TLMYmPMDPDc+3INufcX7wdj/gW59zjzrlQ51wYnv+7Vjjn9Ou1VBrn3H5gr5m1yy26HtjqxZDEt+wBrjaz+rl/L69Hk+RI5VsK3Jn7/E7gYy/GUi51vB1ATeecyzKzscAXeGY/esM5t8XLYYnviAJGAZvMLC637Ann3GdejElE5GzcD7yT+yPnLuAuL8cjPsI5t8bMFgEb8MzavBGY792opDYzs78B1wLNzSwBmARMBT4ws3v9+B5tAAACIElEQVSA/wK/9V6E5WOeoZ4iIiIiIiJS02kIpYiIiIiISC2hBE5ERERERKSWUAInIiIiIiJSSyiBExERERERqSWUwImIiIiIiNQSSuBERMRnmVm2mcXl+zexEusOM7PNlVWfiIhIeeg+cCIi4ssynHOdvR2EiIhIZVEPnIiInHfMbLeZTTezTWa21sza5paHmdkKM4s3s6/M7Be55Reb2Udm9l3uv165Vfmb2V/NbIuZLTezIK/tlIiInBeUwImIiC8LKjSE8rZ876U65yKAOcBLuWUvAwudc5HAO8Ds3PLZwL+cc1cAXYEtueWXAXOdcx2BFODXVbw/IiJynjPnnLdjEBERqRJmluacu6CY8t1AX+fcLjMLAPY755qZ2SEg2DmXmVue5JxrbmbJQKhz7lS+OsKAL51zl+W+fgwIcM5Nrvo9ExGR85V64ERE5HzlSnh+Nk7le56Nri0XEZEqpgRORETOV7fle/w29/k3wPDc5yOAr3OffwWMATAzfzNrXF1BioiI5KdfCkVExJcFmVlcvtf/cM6duZVAUzOLx9OL9rvcsvuBN81sApAM3JVbPh6Yb2b34OlpGwMkVXn0IiIihegaOBEROe/kXgPX3Tl3yNuxiIiInA0NoRQREREREakl1AMnIiIiIiJSS6gHTkREREREpJZQAiciIiIiIlJLKIETERERERGpJZTAiYiIiIiI1BJK4ERERERERGqJ/w+QJfQjuxOWPwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 1080x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"tags":["pdf-inline"],"id":"uv07SgtDTzCI"},"source":["## Inline Question 4:\n","When is layer normalization likely to not work well, and why?\n","\n","1. Using it in a very deep network\n","2. Having a very small dimension of features\n","3. Having a high regularization term\n","\n","\n","## Answer:\n","[FILL THIS IN]\n"]}]}